Tag: wiki-import

396 topic(s)

Multimodal Secure AlignmentMultimodal secure alignment is the problem of making a model's safety behavior consistent across text, images, audio, and mixed-modal inputs. It matters because a model can reconstruct harmful intent across modalities or through images that evade text-only filters, so defenses must align the fused system rather than just one input channel.
Layer Dropping and Progressive Pruning (TrimLLM)Layer dropping and progressive pruning reduce inference cost by cutting transformer depth rather than shrinking every matrix. TrimLLM does this progressively for domain-specialized LLMs, exploiting the empirical fact that not all layers are equally important in a target domain and aiming to retain in-domain accuracy while reducing latency.
Test-Time Compute ScalingTest-time compute scaling improves a model by spending extra computation at inference time, for example through search, verification, reranking, or adaptive refinement, instead of only scaling pretraining. It is most useful on prompts where the base model already has some chance of success, because additional compute can then amplify that success more efficiently than a much larger one-shot model.
GPT-3 & Few-Shot In-Context LearningGPT-3 showed that a 175B-parameter autoregressive Transformer can perform many tasks from natural-language instructions and a few demonstrations in the prompt, without gradient updates or task-specific fine-tuning. That result made in-context learning a central paradigm and showed that scale alone could unlock strong few-shot behavior.
The Bitter LessonThe Bitter Lesson is Sutton's argument that, over the long run, general methods that scale with compute and data outperform systems built around hand-crafted domain knowledge. It is a historical pattern claim, not a theorem, and its force comes from repeated examples in search, game playing, vision, and language.
Constitutional Classifiers++Constitutional Classifiers++ is a production-oriented jailbreak defense that uses context-aware classifiers and a cascade of cheap and expensive checks to block harmful exchanges efficiently. The system is designed to keep refusal rates and serving cost low while still catching universal jailbreaks that earlier, response-only filters missed.
Continuous Thought Machines (CTM)Continuous Thought Machines are models that make neural timing and synchronization part of the representation, instead of treating layers as purely instantaneous mappings. They use neuron-level temporal processing and support adaptive compute, so the same model can stop early on easy inputs or continue reasoning on harder ones.
Mechanistic OOCR Steering VectorsMechanistic OOCR steering vectors are a proposed explanation for some out-of-context reasoning results: fine-tuning can act like adding an approximately constant steering direction to the residual stream, rather than learning a deeply conditional new algorithm. That helps explain why a tuned behavior can generalize far beyond the fine-tuning data and why injecting or subtracting the vector can often reproduce or remove it.
Critical Representation Fine-Tuning (CRFT)Critical Representation Fine-Tuning (CRFT) is a PEFT method that improves reasoning by editing a small set of causally important hidden states instead of updating model weights broadly. It identifies critical representations through information-flow analysis and learns low-rank interventions on those states while keeping the base model frozen.
Chain-of-Thought MonitorabilityChain-of-thought monitorability is the safety claim that when a model needs explicit reasoning to complete a task, its written chain of thought can be monitored for harmful intent or deception. The key property is monitorability rather than perfect faithfulness: hiding the reasoning tends to become harder when the reasoning itself is load-bearing for success.
ZeRO (Zero Redundancy Optimizer)ZeRO (Zero Redundancy Optimizer) partitions optimizer states, gradients, and eventually parameters across data-parallel workers so each GPU no longer stores a full copy of the training state. This cuts memory dramatically and makes very large-model training feasible without requiring full model-parallel architectures.
T5 (Text-to-Text Transfer Transformer)T5 is an encoder-decoder Transformer that casts every NLP task as text-to-text generation, so translation, question answering, classification, and even some regression tasks share the same model and loss. Its span-corruption pretraining on C4 made it a landmark demonstration of unified transfer learning.
GPT-2 & Zero-Shot Task TransferGPT-2 showed that a large decoder-only language model can perform many tasks in the zero-shot setting by continuing a task-formatted prompt rather than being fine-tuned. The key result was that scale and diverse web text made translation, summarization, and question answering look like ordinary next-token prediction.
GPT-1 (Generative Pre-Training)GPT-1 established the pretrain-then-fine-tune recipe for Transformers: first train a decoder on unlabeled text with a language-model objective, then adapt it to downstream tasks with minimal task-specific layers. This showed that generic generative pretraining could beat many bespoke NLP architectures on downstream benchmarks.
ELMo (Embeddings from Language Models)ELMo produces contextualized word embeddings by taking a learned task-specific combination of hidden states from a pretrained bidirectional LSTM language model. Unlike static embeddings such as word2vec or GloVe, it gives the same word different vectors in different sentence contexts.
Sparsely-Gated Mixture of Experts (MoE)A sparsely-gated Mixture of Experts (MoE) layer routes each token to only a small subset of expert networks, so model capacity can grow much faster than compute per token. Its central challenge is routing and load balancing: without auxiliary losses, a few experts tend to monopolize traffic.
Key-Value Memory NetworksKey-Value Memory Networks store each memory slot as a key for retrieval and a separate value for the returned content. This decouples matching from payload and is a direct conceptual precursor to modern query-key-value attention.
Luong Attention (Global and Local)Luong attention is a sequence-to-sequence attention mechanism that scores decoder states against encoder states using multiplicative forms such as dot or bilinear attention. It distinguishes global attention over all source positions from local attention over a predicted window, helping make neural machine translation more scalable.
GloVe Word EmbeddingsGloVe learns word embeddings by fitting vector dot products to the log of global word-word co-occurrence counts. Because it is trained on ratios of co-occurrence statistics, linear relations such as king minus man plus woman approximately equals queen often emerge in the embedding space.
Neural Turing Machine (NTM)A Neural Turing Machine augments a neural controller with a differentiable external memory that it can read from and write to using soft attention over memory locations. It was an early attempt to learn algorithm-like behavior such as copying and sorting while remaining trainable end to end.
Xavier/Glorot InitializationXavier or Glorot initialization chooses weight variance from fan-in and fan-out so activations and gradients stay roughly stable across deep layers. It is well suited to symmetric activations such as tanh, while ReLU networks usually prefer He initialization.
ImageNet DatasetImageNet is a large, hierarchically labeled image dataset whose 1000-class ILSVRC benchmark became the defining testbed for modern computer vision. AlexNet's 2012 win on ImageNet triggered the deep learning shift by showing that GPU-trained CNNs could dramatically beat hand-engineered pipelines.
Neural Probabilistic Language ModelThe Neural Probabilistic Language Model replaced count-based n-grams with learned word embeddings and a neural network that predicts the next word from a continuous representation of context. Its core contribution was showing that distributed representations let language models generalize to unseen but similar word sequences.
Weight TyingWeight tying uses the same matrix for token embeddings and the output softmax projection, typically by setting the output weights to the transpose of the input embedding table. This cuts parameters and often improves language modeling by forcing input and output token representations to share geometry.
Gradient Checkpointing (Activation Recomputation)Gradient checkpointing saves memory by storing only selected activations during the forward pass and recomputing the missing ones during backpropagation. The trade-off is extra compute for lower peak memory, which is why it is widely used to train large Transformers that would otherwise not fit in GPU memory.
PagedAttentionPagedAttention stores the KV cache in fixed-size non-contiguous blocks, like virtual-memory pages, instead of requiring one contiguous allocation per sequence. This largely removes fragmentation, enables prompt-prefix sharing, and is a key reason vLLM can serve many more concurrent requests.
Speculative DecodingSpeculative decoding speeds up autoregressive generation by letting a small draft model propose several tokens and then having the large target model verify them in parallel. With the rejection-sampling correction from the original algorithm, the output distribution remains exactly the same as sampling from the target model alone.
KL-Divergence Penalty in RLHFThe KL-divergence penalty in RLHF keeps the learned policy close to a reference model while it maximizes reward, usually by subtracting a term proportional to the KL divergence from the objective. This stabilizes training and reduces reward hacking by discouraging the policy from drifting too far from fluent supervised behavior.
Proximal Policy Optimization (PPO)Proximal Policy Optimization is a policy-gradient algorithm that improves a policy while clipping how far action probabilities can move from the previous policy in one update. In RLHF it is usually paired with a KL penalty so the model gains reward without drifting too far from a reference model.
Next-Token Prediction Objective (Causal Language Modeling)Next-token prediction trains a causal language model to assign high probability to each token given all previous tokens. Maximizing this likelihood over large text corpora teaches the model syntax, facts, and reusable patterns that later support prompting and generation.
AdamW OptimizerAdamW is Adam with decoupled weight decay: parameter shrinkage is applied directly to the weights instead of being mixed into the adaptive gradient update. This preserves the intended regularization effect and is why AdamW became the default optimizer for many Transformer models.
SwiGLU Activation FunctionSwiGLU is a gated feed-forward activation that multiplies one linear projection by a Swish-activated gate from another projection. It usually performs better than standard ReLU-style MLP blocks at similar scale, which is why many modern LLMs use it in their feed-forward layers.
Pre-Norm vs. Post-Norm ArchitecturePre-Norm vs. Post-Norm is the choice of whether layer normalization is applied before or after each residual sublayer in a Transformer block. Pre-Norm usually trains deeper stacks more stably by preserving gradient flow through the residual path, while Post-Norm was the original design and can be less stable at scale.
Byte Pair Encoding (BPE)Byte Pair Encoding is a subword tokenization method that repeatedly merges the most frequent adjacent symbols in a corpus. It builds a vocabulary between characters and whole words, which handles rare words better than word-level tokenization while keeping sequence lengths manageable.
Softmax TemperatureSoftmax temperature rescales logits before softmax to control randomness in the output distribution. Lower temperature makes probabilities sharper and decoding more deterministic, while higher temperature flattens the distribution and increases diversity.
Key-Value (KV) CachingKey-value caching stores the attention keys and values from earlier tokens during autoregressive decoding so they do not need to be recomputed at every step. It speeds up generation dramatically, but the cache grows with sequence length and turns inference into a memory-management problem.
Rotary Positional Embedding (RoPE)Rotary Positional Embedding encodes position by rotating query and key vectors with token-index-dependent angles before attention is computed. Because the resulting dot products depend on relative offsets, RoPE gives Transformers a simple and widely used way to represent order.
Sinusoidal Positional EncodingSinusoidal positional encoding adds fixed sine and cosine patterns of different frequencies to token embeddings so the model can infer token order. The encoding is deterministic and smooth across positions, which let the original Transformer represent position without learning a separate table.
Causal (Masked) Self-AttentionCausal masked self-attention is self-attention with a mask that prevents each position from attending to future tokens. Applying the mask before softmax enforces autoregressive order, so the model can predict the next token without seeing the answer in advance.
Message Passing in Graph Neural Networks (GNNs)Message passing in graph neural networks updates each node by aggregating transformed information from its neighbors and combining it with the node's current representation. After K rounds, a node's state depends on its K-hop neighborhood, which is why message passing is the core operation of most spatial GNNs.
Triplet Margin LossTriplet margin loss trains an embedding space so an anchor is closer to a positive example than to a negative example by at least a fixed margin. It is a standard metric-learning objective because it directly enforces relative similarity rather than predicting class labels.
Lagrange MultipliersLagrange multipliers solve constrained optimization problems by introducing auxiliary variables that encode the constraints inside a single objective. At a constrained optimum, the gradient of the objective lies in the span of the constraint gradients, which is why the method is central to duality and SVM derivations.
Ordinary Least Squares (OLS) Closed-Form SolutionThe OLS closed-form solution is the exact least-squares answer computed directly from the design matrix rather than by iterative optimization. In the full-rank case it solves the normal equations, and geometrically it projects the target vector onto the column space of the features.
PAC Learning (Probably Approximately Correct)PAC learning formalizes what it means for a hypothesis class to be learnable: with enough samples, an algorithm should return a hypothesis whose error is small with high probability. It is foundational because sample complexity and model capacity can then be expressed as rigorous guarantees instead of heuristics.
Receiver Operating Characteristic (ROC) & AUCThe ROC curve plots true positive rate against false positive rate as a binary classifier's threshold changes, and AUC summarizes that curve into a single number. AUC also has a ranking interpretation: it is the probability that a random positive example scores above a random negative one.
Hidden Markov Model (HMM)A Hidden Markov Model is a sequence model with an unobserved Markov chain of states and an observed emission distribution from each state. It became a standard model for speech, tagging, and other structured sequence tasks because dynamic programming can efficiently infer likely states and sequence probabilities.
Convex FunctionA convex function is one whose value on any line segment lies below the chord connecting that segment's endpoints. This matters in optimization because convex problems have no spurious local minima: every local minimum is global.
Shannon EntropyShannon entropy measures the expected surprisal of a random variable and quantifies how uncertain its outcomes are. It is the basic information-theoretic quantity from which cross-entropy, KL divergence, mutual information, and many ML loss functions are built.
L1 vs. L2 NormsThe L1 norm sums absolute values and tends to promote sparsity when used as a penalty, while the L2 norm measures Euclidean length and tends to shrink weights smoothly without zeroing many of them out. That difference is why L1 is associated with feature selection and L2 with stable shrinkage.
K-Means Objective Function (Inertia)The K-means objective, also called inertia, is the sum of squared distances from each point to its assigned cluster centroid. K-means greedily lowers that objective by alternating between reassigning points and recomputing centroids, though the result still depends on initialization because the problem is nonconvex.
The Curse of DimensionalityThe curse of dimensionality is the collection of high-dimensional effects that make data sparse, neighborhoods less informative, and sample requirements explode as dimension grows. It helps explain why distance-based methods, density estimation, and exhaustive search often break down in large feature spaces.
Markov Chain Monte Carlo (MCMC)Markov Chain Monte Carlo samples from a difficult target distribution by constructing a Markov chain whose stationary distribution matches that target. It is essential in Bayesian inference because it replaces intractable posterior integrals with averages over samples, provided the chain mixes well enough.
Reparameterization Trick (VAE)The reparameterization trick writes a stochastic latent sample as a differentiable transformation of parameters and noise, typically z equals mu plus sigma times epsilon. This lets gradients flow through sampling and makes variational autoencoder training practical with backpropagation.
AutoencodersAutoencoders are neural networks trained to reconstruct their inputs after passing them through a compressed or otherwise constrained latent representation. They are useful because the bottleneck forces the model to learn structure in the data rather than just memorize an identity map.
GAN Minimax ObjectiveThe GAN minimax objective sets up a two-player game in which a generator tries to produce samples that fool a discriminator, while the discriminator tries to distinguish real from generated data. At equilibrium the generator matches the data distribution, though the training game is often unstable in practice.
Q-LearningQ-learning is an off-policy reinforcement learning algorithm that learns the optimal action-value function by bootstrapping from a Bellman target over the best next action. Because its update does not require following the current policy, it became a foundational method in both tabular RL and DQN-style deep RL.
The Bellman EquationThe Bellman equation recursively expresses the value of a state or state-action pair as immediate reward plus discounted expected future value. It is the backbone of dynamic programming and reinforcement learning because it turns long-horizon return into a local consistency condition.
The Markov PropertyThe Markov property says that the conditional distribution of the future depends only on the present state, not on the full past history, once the state is known. It is the defining assumption behind Markov chains and MDPs and tells you when a state representation is sufficient for planning.
Bootstrap Aggregating (Bagging)Bootstrap Aggregating trains multiple models on bootstrap-resampled versions of the training set and averages their predictions to reduce variance. It helps most with unstable base learners such as decision trees, which is why it underlies random forests.
Gradient Boosting Machines (GBM)Gradient Boosting Machines build an additive model by fitting each new weak learner to the negative gradient of the current loss. In practice each stage focuses on correcting the remaining errors of the ensemble, which makes boosting powerful but sensitive to overfitting if trees and learning rates are not controlled.
The Fisher Information MatrixThe Fisher Information Matrix measures how sensitive a model's log-likelihood is to changes in its parameters and therefore captures local statistical curvature. It underlies asymptotic variance bounds and natural gradient methods because it defines a geometry tied to the model's predictive distribution.
AdaBoost (Adaptive Boosting)AdaBoost builds an ensemble by repeatedly fitting weak learners to reweighted data so that previously misclassified examples receive more attention. Its final predictor is a weighted vote of the learners, and its power comes from turning many slightly better-than-random classifiers into a strong one.
Information Gain (Decision Trees)Information gain is the reduction in entropy achieved by splitting a dataset on a candidate feature. Decision-tree algorithms use it to choose splits that most reduce label uncertainty, though raw information gain can be biased toward features with many distinct values.
DropoutDropout regularizes a neural network by randomly zeroing activations during training, which prevents units from co-adapting too strongly. At test time the full network is used with rescaled activations, making dropout behave like an inexpensive ensemble-style regularizer.
Singular Value Decomposition (SVD)Singular Value Decomposition factors any matrix into orthogonal directions and nonnegative singular values. It is fundamental because low-rank approximation, PCA, pseudoinverses, compression, and many denoising methods all follow from that decomposition.
Maximum A Posteriori (MAP) EstimationMaximum a posteriori estimation chooses the parameter value that maximizes posterior probability given the data. It is equivalent to maximum likelihood plus a log-prior regularizer, which is why MAP connects Bayesian estimation to familiar penalized optimization objectives.
Supervised learningSupervised learning trains a model on labeled input-output pairs so it can predict the correct target on new examples from the same distribution. Classification and regression are its two main forms, depending on whether the target is discrete or continuous.
Unsupervised learningUnsupervised learning tries to discover structure in data without labeled targets, such as clusters, latent factors, or a density model. It is used for representation learning, dimensionality reduction, clustering, and generative modeling when explicit supervision is unavailable.
Reinforcement learning (RL)Reinforcement learning studies how an agent should act through trial and error to maximize cumulative reward in an environment. Unlike supervised learning, feedback is delayed and depends on the agent's own actions, so the problem is about sequential decision-making as much as prediction.
Linear functionA linear function satisfies additivity and homogeneity, so it can be written as a matrix map with no bias term. In machine learning people often use 'linear' loosely for affine maps, but mathematically the distinction matters because adding a bias breaks true linearity.
Affine transformationAn affine transformation is a linear map followed by a translation, so it has weights and a bias. Dense neural network layers are affine rather than strictly linear, because the bias lets the model shift activations and decision boundaries.
Loss functionA loss function maps a model's prediction and the true target to a scalar error signal that training aims to minimize. It defines what the model is optimized for, so changing the loss changes which mistakes are treated as costly.
Mean squared error (MSE)Mean squared error averages the squared difference between predicted and true values, making large errors count disproportionately more than small ones. For regression it is especially important because minimizing MSE is equivalent to maximum likelihood under Gaussian noise.
Prediction errorPrediction error is the difference between a model's prediction and the true target for an example. It is the atomic quantity from which losses, residual analysis, and generalization metrics are built.
Gradient descentGradient descent minimizes a differentiable objective by repeatedly moving parameters in the direction of steepest local decrease, namely the negative gradient. Its step size is set by the learning rate, so convergence depends on both objective geometry and update scale.
Mini-batch gradient descentMini-batch gradient descent estimates the gradient on a small subset of training examples at each update instead of on the full dataset or a single example. It is the practical default in deep learning because it balances hardware efficiency with optimization noise.
Stochastic gradient descent (SGD)Stochastic gradient descent updates parameters using a gradient estimate from one example or a very small random batch, making each step noisy but cheap. That noise can slow exact convergence yet often helps large models optimize and generalize in practice.
ConvergenceIn optimization, convergence means an algorithm's iterates approach a stable solution or stationary point as updates continue. In practice people often mean that the loss or parameters stop changing much, though true convergence depends on the objective and algorithmic assumptions.
GeneralizationGeneralization is a model's ability to perform well on unseen data from the same underlying distribution as its training data. It is the real goal of learning, because low training error alone can come from memorization rather than useful structure.
RegularizationRegularization is any technique that biases learning toward simpler, more stable, or less overfit solutions. It can appear as an explicit penalty such as weight decay or as an implicit training choice such as data augmentation, dropout, or early stopping.
L1 regularization (Lasso)L1 regularization adds a penalty proportional to the sum of absolute parameter values, encouraging many coefficients to become exactly zero. That sparsity makes Lasso useful when feature selection is part of the goal, not just shrinkage.
L2 regularization (Ridge/Weight Decay)L2 regularization adds a penalty proportional to the sum of squared parameter values, shrinking weights toward zero without usually making them exactly sparse. In plain SGD it is equivalent to weight decay and is widely used because it improves stability and reduces variance.
Early stoppingEarly stopping regularizes training by halting optimization when validation performance stops improving and keeping the best checkpoint seen so far. It works because prolonged optimization can eventually fit noise or idiosyncrasies of the training set rather than signal.
HyperparameterA hyperparameter is a setting chosen outside the optimization loop, such as learning rate, model width, regularization strength, or batch size. Unlike learned parameters, hyperparameters govern how the model is trained or structured and are usually selected by validation.
Data leakageData leakage occurs when information that would not be available at prediction time leaks into training or model selection, causing overly optimistic evaluation. Common examples are fitting preprocessing on the full dataset, peeking at test labels, or using future information in time-series tasks.
BackpropagationBackpropagation computes gradients of a scalar loss with respect to all network parameters by applying the chain rule backward through the computation graph. It makes deep learning practical because it turns a complicated nested function into reusable local gradient calculations.
Forward passThe forward pass is the computation that maps input data through the model to produce activations and an output prediction. During training it also caches intermediate values needed later by the backward pass.
Backward passThe backward pass propagates gradients from the loss back through the computation graph to determine how each parameter affected the final error. It uses stored forward-pass intermediates and the chain rule to accumulate derivatives efficiently.
Chain ruleThe chain rule gives the derivative of a composition of functions by multiplying local derivatives along the computation path. It is the mathematical principle that backpropagation applies at scale throughout a neural network.
Automatic differentiationAutomatic differentiation computes exact derivatives of a program by systematically composing derivatives of its primitive operations. Unlike symbolic differentiation it does not manipulate formulas, and unlike numerical differentiation it does not rely on finite-difference approximations.
Computational graphA computational graph represents a calculation as nodes for variables or operations and edges for data dependencies. It is useful because the same graph that defines the forward computation can also be traversed backward to perform automatic differentiation.
Neural networkA neural network is a parameterized function built by composing affine transformations with nonlinear activations across layers. Its power comes from learning representations from data rather than relying on hand-crafted features for each task.
Learning rateThe learning rate is the scalar that sets how large each optimization step is when parameters are updated. If it is too high training can diverge or oscillate, and if it is too low training can become extremely slow or get stuck.
EpochAn epoch is one complete pass through the training dataset. In mini-batch training, an epoch consists of many updates, one for each batch needed to cover the data once.
OverfittingOverfitting happens when a model fits patterns specific to the training set, including noise, better than it captures the underlying data-generating structure. The usual symptom is low training error paired with substantially worse validation or test error.
UnderfittingUnderfitting happens when a model is too limited, too constrained, or too poorly trained to capture the main structure in the data. It usually shows up as high error on both training and validation data, indicating high bias rather than variance.
NeuronA neuron in a neural network computes a weighted sum of its inputs, adds a bias, and applies an activation function. Collections of neurons form layers, so a single neuron's role is simple even though many together can represent complex functions.
Activation functionAn activation function is the nonlinear mapping applied after an affine transformation in a neural network. It is what prevents a stack of layers from collapsing into one affine map, enabling deep networks to approximate complex functions.
SigmoidThe sigmoid function maps a real number to a value between zero and one, making it easy to interpret as a probability or gate. Its downside is saturation at large positive or negative inputs, which can cause vanishing gradients in deep networks.
TanhThe tanh function maps inputs to the range minus one to one and is zero-centered, which often makes optimization easier than with the sigmoid. Like the sigmoid, however, it still saturates at large magnitudes and can cause vanishing gradients.
ReLUReLU outputs the positive part of its input and zero otherwise. It became the default activation in many deep networks because it is simple, cheap, and far less prone to saturation than sigmoid or tanh, though units can still die if they stay on the zero side.
SoftmaxSoftmax turns a vector of logits into a probability distribution by exponentiating and normalizing them so the components sum to one. It is commonly used for multiclass prediction because it converts arbitrary scores into class probabilities while preserving their ranking.
Fully connected layerA fully connected layer applies an affine transformation in which every output unit depends on every input feature. It is the standard dense layer used in multilayer perceptrons and as a projection block inside many larger architectures.
Input layerThe input layer is the entry point of a network, where raw or preprocessed features are presented to the model. Unlike hidden layers, it usually performs little or no learned computation by itself and mainly defines the representation the rest of the network receives.
Hidden layerA hidden layer is any internal layer between the input and output of a network. Hidden layers transform raw inputs into increasingly useful intermediate representations that the final output layer can read out.
Output layerThe output layer is the final transformation that maps a model's last hidden representation to a prediction space such as class logits, probabilities, or regression values. Its shape and activation depend on the task being solved.
Multilayer perceptron (MLP)A multilayer perceptron is a feedforward neural network made of stacked fully connected layers and nonlinear activations. It is the canonical dense architecture for tabular function approximation and the feed-forward subnetwork inside many Transformer blocks.
Deep neural networkA deep neural network is a neural network with multiple hidden layers rather than just one or two. The extra depth lets it build hierarchical features and represent complex functions more efficiently than shallow networks in many settings.
Deep learningDeep learning is the study and practice of training multilayer neural networks on data to learn useful representations automatically. Its hallmark is end-to-end learning of features and predictors together, especially when large data and compute are available.
Composite functionA composite function applies one function to the output of another, such as f of g of x. Neural networks are composite functions at scale, which is why gradients are computed by repeatedly applying the chain rule.
Feedforward neural networkA feedforward neural network is a network whose computations move from input to output without recurrent cycles. Each layer depends only on earlier activations in the same pass, making feedforward networks the basic template for MLPs and many vision models.
Convolutional neural network (CNN)A convolutional neural network uses learned convolution filters with local receptive fields and weight sharing to process grid-like data such as images. Those inductive biases make CNNs especially effective and parameter-efficient for visual pattern recognition.
Recurrent neural network (RNN)A recurrent neural network processes sequences by maintaining a hidden state that is updated one step at a time from the current input and previous state. This gives it a notion of temporal memory, but plain RNNs are hard to train on long dependencies because gradients can vanish or explode.
Elman RNNAn Elman RNN is the classic simple recurrent network in which the next hidden state is a nonlinear function of the current input and previous hidden state. It introduced the basic hidden-state recurrence used by later gated models, but long-range memory is poor without gating.
Hidden stateThe hidden state is the internal representation a sequential model carries forward as it processes inputs over time. In an RNN or LSTM it summarizes relevant past context, and in broader neural architectures it usually means a layer's intermediate activation vector.
Backpropagation through time (BPTT)Backpropagation through time trains a recurrent network by unrolling it across sequence steps and applying backpropagation to the resulting deep computational graph. It exposes how earlier states influence later losses, but long unrolls make optimization and memory use difficult.
What is the vanishing gradient problem?The vanishing gradient problem is the tendency for gradients propagated through many layers or time steps to shrink exponentially, making early parameters learn extremely slowly. It is especially severe in deep sigmoid or tanh networks and was a main motivation for LSTMs, better initialization, and residual connections.
Long short-term memory (LSTM)Long short-term memory is a gated recurrent architecture designed to preserve information over long timescales. Its input, forget, and output gates regulate a cell state with near-linear self-connections, which helps prevent the vanishing-gradient behavior of simple RNNs.
xLSTMxLSTM is a family of modern LSTM variants that adds exponential gating and redesigned memory structures, including scalar-memory and matrix-memory forms, to make recurrent models more scalable. The goal is to keep LSTM-style recurrence while improving stability, parallelism, and long-context performance.
minLSTMminLSTM is a simplified LSTM variant designed to remove some of the sequential dependencies that make classical LSTMs expensive while keeping useful gating behavior. The result is a lighter recurrent block that can be trained more efficiently and scaled more easily.
Gated recurrent unit (GRU)A gated recurrent unit is a recurrent architecture that uses update and reset gates to control how much past information is kept and how much new input is written into the hidden state. It is simpler than an LSTM because it has no separate cell state, yet it often achieves similar sequence-modeling performance.
Embedding layerAn embedding layer maps discrete IDs such as words, subwords, or items to learned dense vectors. It is essential whenever symbolic inputs must be represented in a continuous space that gradient-based models can manipulate.
Embedding vectorAn embedding vector is the dense continuous representation assigned to a discrete token, item, or entity by an embedding table or model. Its meaning comes from geometry: similar entities tend to occupy nearby directions or neighborhoods in the learned space.
Word embeddingA word embedding is a dense vector representation of a word learned from distributional context rather than hand-coded features. Its purpose is to place semantically or syntactically related words near one another in vector space so downstream models can generalize across vocabulary items.
Word2VecWord2Vec is a family of shallow neural methods that learn word embeddings from local context, most famously via the skip-gram and CBOW objectives. Its importance is that simple predictive training on large text corpora produced useful semantic geometry, including analogy-like linear regularities.
Skip-gramSkip-gram trains a model to predict surrounding context words from a center word. It learns embeddings that are especially good for capturing rare-word semantics because each observed word directly becomes a prediction source for many context targets.
FastTextFastText extends Word2Vec by representing a word as a bag of character n-gram embeddings rather than as a single atomic vector. That lets it model morphology and produce reasonable embeddings for rare or even unseen words.
Semantic similaritySemantic similarity is the degree to which two words, sentences, or documents share meaning rather than just surface form. In machine learning it is often estimated with embeddings and cosine similarity, which turns meaning comparison into a geometric problem.
Cosine similarityCosine similarity measures the angle between two vectors: \( \cos \theta = x \cdot y / (\|x\| \|y\|) \). It ignores magnitude and compares direction, which is why it is the default similarity metric for embeddings in retrieval, clustering, and semantic search.
Bag of wordsBag of words represents a document by counts or weights of vocabulary terms while discarding word order and syntax. It is simple, sparse, and historically central to information retrieval and document classification, but it cannot distinguish sentences with the same words in different orders.
Document-term matrixA document-term matrix is a matrix whose rows are documents, columns are vocabulary terms, and entries are counts or weights such as TF-IDF. It is the core data structure behind bag-of-words retrieval, topic modeling, and many classical NLP pipelines.
TF-IDFTF-IDF weights a term by how frequent it is in a document and how rare it is across the corpus, typically \( tf(w,d) \log(N/df(w)) \). It downweights ubiquitous words and highlights terms that are especially informative for a given document.
SparsitySparsity means most entries in a vector, matrix, or parameter set are exactly zero. In ML it matters because sparse representations save memory and computation, and because sparsity-inducing penalties such as L1 can make models more interpretable.
Dense vectorA dense vector is a low- or moderate-dimensional representation in which most entries are nonzero. Dense vectors are usually learned embeddings, so they capture semantic similarity better than sparse count vectors but are harder to interpret directly.
Sparse vectorA sparse vector has very few nonzero entries relative to its dimensionality. Classical text features such as bag-of-words and TF-IDF are sparse, which makes them memory-efficient and interpretable even when the feature space is huge.
One-hot encodingOne-hot encoding represents a categorical variable as a binary vector with exactly one 1 and all other entries 0. It preserves category identity without implying any ordering, but its dimensionality grows linearly with the number of categories.
TokenizationTokenization is the process of splitting raw text into model-readable tokens such as words, subwords, bytes, or characters. It determines vocabulary size, sequence length, and how efficiently a language model handles rare words, multilingual text, and code.
TokenA token is the discrete unit a language model reads and predicts. Depending on the tokenizer, a token may be a word, subword, byte, punctuation mark, or special control symbol, and token count determines both context usage and API cost.
SubwordA subword is a token unit smaller than a full word but larger than a character, learned to balance vocabulary size against sequence length. Subword tokenization lets models handle rare and novel words by composing them from reusable pieces.
VocabularyA vocabulary is the fixed set of tokens a tokenizer can map text into and a model can natively process. Its size trades off compression against flexibility: larger vocabularies shorten sequences, while smaller ones rely more on subword or byte composition.
CorpusA corpus is a structured collection of text used to train, fine-tune, or evaluate language models. Its size, quality, domain mix, and cleaning decisions strongly shape what a model knows, how it generalizes, and which biases it inherits.
N-gramAn n-gram is a contiguous sequence of \( n \) tokens, such as a bigram for \( n=2 \) or trigram for \( n=3 \). N-grams are the basic units of classical language models and many text features because they capture short-range local context.
Count-based language modelA count-based language model estimates sequence probabilities from n-gram counts in a corpus, then uses smoothing or backoff for unseen events. It was the dominant pre-neural approach to language modeling, but it struggles with long context and data sparsity.
Language modelA language model assigns probabilities to token sequences, or equivalently predicts missing or next tokens from context. This unifies classical n-gram models, masked models like BERT, and autoregressive LLMs such as GPT under one probabilistic framework.
Autoregressive language modelAn autoregressive language model generates text left-to-right by modeling \( P(w_t \mid w_{<t}) \) for each token. Because it only conditions on past tokens, it can be used directly for open-ended generation as well as scoring sequences.
Masked language modelA masked language model is trained to recover tokens hidden within a sequence using both left and right context. This bidirectional training makes MLMs strong encoders for understanding tasks, but less natural than autoregressive models for direct generation.
Causal language modelA causal language model predicts each token using only earlier tokens, enforced by a causal attention mask. It is essentially the same modeling family as an autoregressive language model, with the word 'causal' emphasizing the masking constraint in self-attention.
Chat language modelA chat language model is a pretrained LLM further tuned to follow instructions and handle multi-turn dialogue. It is usually built by supervised fine-tuning plus preference optimization or RLHF, so it behaves more helpfully and safely than the raw base model.
PerplexityPerplexity is the exponentiated average negative log-likelihood of a test sequence, so lower perplexity means the model is less surprised by the data. It is a standard intrinsic metric for language models, though low perplexity does not guarantee downstream usefulness.
Log-likelihoodLog-likelihood is the logarithm of the probability a model assigns to the observed data under given parameter values. Taking logs turns products into sums, making estimation numerically stable and turning maximum likelihood into a tractable optimization problem.
Negative log-likelihoodNegative log-likelihood is the loss obtained by negating the log-likelihood, so maximizing probability becomes minimizing a positive objective. It is the standard training loss for probabilistic classifiers, language models, and many generative models.
Maximum likelihood estimate (MLE)The maximum likelihood estimate selects the parameter values that make the observed data most probable under the model. Many standard ML objectives, including cross-entropy for classification and next-token prediction for LLMs, are just MLE written as minimization of negative log-likelihood.
Laplace smoothingLaplace smoothing adds a small constant, often 1, to every discrete count before normalizing probabilities. It prevents zero-probability events in models such as naive Bayes and n-gram LMs, though it can over-smooth when the vocabulary is large.
Conditional probabilityConditional probability is the probability of an event after restricting attention to cases where another event is known to occur, written \( P(A \mid B) = P(A,B)/P(B) \). It is the basic object behind Bayes' rule, autoregressive models, and all context-dependent prediction.
Probability distributionA probability distribution specifies how probability mass or density is assigned over the possible outcomes of a random variable. In ML it is the central object because models learn, approximate, or transform distributions over labels, latent variables, sequences, or data.
Discrete probability distributionA discrete probability distribution assigns nonnegative probabilities to a countable set of outcomes that sum to 1. Softmax outputs in classification and next-token prediction are discrete distributions over labels or vocabulary items.
Backoff (N-gram backoff)Backoff is an n-gram smoothing strategy that uses a high-order estimate when it has enough evidence and otherwise falls back to a lower-order n-gram. It handles sparsity by preferring specific context when available without assigning zero probability to unseen sequences.
Zipf's lawZipf's law says a word's frequency is roughly inversely proportional to its rank in the frequency table. This heavy-tailed structure explains why a few tokens dominate corpora, why vocabularies keep growing with more data, and why tokenization and smoothing are central in NLP.
Cross-entropyCross-entropy measures the average coding cost of samples from a true distribution \( p \) when encoded using a model distribution \( q \). In ML it is the standard loss for classification and language modeling, and minimizing it is equivalent to maximum likelihood up to an entropy constant.
Binary cross-entropyBinary cross-entropy is the cross-entropy loss for a Bernoulli target, typically \( -[y\log \hat p + (1-y)\log(1-\hat p)] \). It is the standard loss for binary classification and for multi-label problems where each label is predicted independently.
LogitA logit is the raw score before sigmoid or softmax normalization. In binary settings, the logit is also the log-odds \( \log\frac{p}{1-p} \), which is why linear models such as logistic regression operate naturally in logit space.
ClassificationClassification is a supervised learning task in which the target is a discrete label rather than a continuous value. The model learns decision boundaries that separate classes, often outputting calibrated class probabilities as well as the predicted label.
Binary classificationBinary classification is classification with exactly two classes, usually framed as predicting the probability of a positive class. It is commonly trained with logistic regression or a sigmoid output and binary cross-entropy loss.
Multiclass classificationMulticlass classification assigns each input to exactly one of \( K>2 \) mutually exclusive classes. Models usually produce a softmax distribution over classes and train with cross-entropy against a one-hot or label-smoothed target.
RegressionRegression is a supervised learning task where the target is continuous rather than categorical. The model predicts a numeric value, and common losses such as mean squared error correspond to assumptions about the noise model, especially Gaussian noise.
Linear RegressionLinear regression models a target as an affine function of the inputs, typically \( y \approx w^\top x + b \), and fits the parameters by minimizing squared residuals. It is the canonical baseline for regression because it is interpretable and often has a closed-form OLS solution.
Logistic RegressionLogistic regression is a linear classifier that models the log-odds of a class as \( w^\top x + b \) and maps that score through a sigmoid to get a probability. Despite its name, it is a classification model, not a regression model.
AccuracyAccuracy is the fraction of predictions that are correct, \( (\text{TP}+\text{TN})/N \). It is intuitive and useful when classes are balanced, but it can be badly misleading on imbalanced datasets where always predicting the majority class already yields high accuracy.
PrecisionPrecision is the fraction of predicted positives that are truly positive, \( \text{TP}/(\text{TP}+\text{FP}) \). It matters most when false positives are costly, such as spam filters, safety classifiers, or medical screening follow-ups.
RecallRecall is the fraction of actual positives that the model successfully retrieves, \( \text{TP}/(\text{TP}+\text{FN}) \). It matters most when missing positives is costly, such as fraud detection, disease screening, or retrieval systems where relevant items should not be overlooked.
F1 ScoreF1 score is the harmonic mean of precision and recall, \( 2PR/(P+R) \). It is high only when both precision and recall are high, making it useful for imbalanced classification where accuracy hides the trade-off between false positives and false negatives.
ROUGEROUGE is a family of overlap metrics for summarization and generation, based on matching n-grams, longest common subsequences, or skip-bigrams between a candidate and reference text. It measures lexical recall more than semantic faithfulness, so it is informative but limited.
Longest Common SubsequenceThe longest common subsequence is the longest sequence of symbols that appears in two sequences in the same order, not necessarily contiguously. It underlies edit-distance-style dynamic programming and metrics such as ROUGE-L because it captures shared sequence structure beyond exact n-gram matches.
Edit DistanceEdit distance is the minimum number of insertions, deletions, and substitutions needed to transform one sequence into another. The most common version, Levenshtein distance, is a dynamic-programming measure of string similarity used in spelling correction, alignment, and evaluation.
PerceptronThe perceptron is a linear threshold classifier that predicts a class from the sign of \( w^\top x + b \) and updates its weights only on mistakes. It is historically important because it introduced gradient-like learning for linear separators, but it only converges when the data are linearly separable.
Decision TreeA decision tree predicts by recursively splitting the feature space with if-then tests until a leaf assigns a class or value. Trees are easy to interpret and capture nonlinearity, but a single deep tree has high variance and overfits without pruning or ensembling.
Random ForestA random forest is an ensemble of decision trees trained on bootstrap samples with random feature subsetting at each split. Averaging many decorrelated trees greatly reduces variance, which is why random forests are strong tabular baselines with little tuning.
Support Vector Machine (SVM)A support vector machine finds the decision boundary that maximizes the margin between classes, depending only on the support vectors nearest the boundary. With kernels, SVMs can model nonlinear separators while retaining a convex optimization objective.
Kernel MethodsKernel methods turn linear algorithms into nonlinear ones by replacing inner products with a kernel function that implicitly measures similarity in a higher-dimensional feature space. This is the core trick behind SVMs, kernel ridge regression, and Gaussian processes.
Principal Component Analysis (PCA)Principal component analysis finds orthogonal directions of maximal variance in the data and projects onto the top few of them. It is a linear dimensionality-reduction method that compresses data, denoises features, and reveals dominant global structure through eigenvectors of the covariance matrix.
Dimensionality ReductionDimensionality reduction maps data into fewer dimensions while preserving as much important structure as possible, such as variance, distances, or neighborhood relations. It is used for compression, visualization, denoising, and making downstream learning easier in high-dimensional spaces.
TransformerA Transformer is a sequence model built from self-attention, position-wise MLPs, residual connections, and normalization, rather than recurrence or convolution. Its key advantage is that every token can directly attend to every other token in parallel, which made modern LLM scaling practical.
Decoder BlockA decoder block is the basic unit of a decoder-only Transformer: causal self-attention plus a position-wise MLP, wrapped with residual connections and normalization. Stacking these blocks lets the model mix context across tokens while preserving autoregressive generation.
Decoder-only TransformerA decoder-only Transformer is a Transformer architecture composed only of masked self-attention blocks, so each token can attend only to earlier tokens. This makes it the standard architecture for autoregressive language models such as GPT, LLaMA, and Claude.
Self-AttentionSelf-attention lets each token compute a weighted combination of representations from other tokens in the same sequence, with weights determined by query-key similarity. It is the mechanism that gives Transformers flexible, content-dependent context mixing without recurrence.
Attention ScoreAn attention score is the compatibility value computed between a query and a key before normalization, often by dot product or a learned variant. Higher scores mean the corresponding token or memory slot should receive more weight after the softmax.
What is a scaled attention score?A scaled attention score is a query-key dot product divided by \( \sqrt{d_k} \) before softmax. The scaling keeps the variance of the logits from growing with key dimension, which helps prevent softmax saturation and keeps gradients well behaved.
Masked Attention ScoreA masked attention score is an attention logit after adding a mask that blocks forbidden positions, typically by adding a very large negative value before softmax. This forces the resulting attention weight to be effectively zero at those positions.
Attention WeightsAttention weights are the normalized coefficients, usually produced by a softmax over attention scores, that determine how much each value vector contributes to the output. They form a distribution over positions or memory entries for each query.
Attention MaskAn attention mask is a tensor that tells an attention layer which positions may interact and which must be blocked. It is used for causal generation, padding suppression, and task-specific visibility patterns, and it must be applied before softmax, not after.
Causal MaskA causal mask blocks attention to future positions by masking entries above the sequence diagonal. It enforces left-to-right autoregressive prediction, ensuring that token \( t \) can depend only on tokens \( \le t \).
Multi-Head AttentionMulti-head attention runs several attention mechanisms in parallel on different learned projections of the same input, then concatenates their outputs. This lets the model capture multiple relational patterns at once instead of forcing all interactions through a single attention map.
Attention HeadAn attention head is one parallel query-key-value attention computation inside multi-head attention. Different heads can specialize to different patterns, such as local syntax, long-range dependencies, or induction-like copying behavior.
Grouped-Query Attention (GQA)Grouped-query attention shares key and value heads across groups of query heads, reducing KV-cache size and bandwidth during inference. It sits between full multi-head attention and multi-query attention, preserving most quality while making long-context serving cheaper.
Query, Key, Value (QKV)Query, key, and value are the three learned projections used by attention: the query asks what to look for, the key says what each position offers, and the value is the content returned if that position is attended to. Attention weights come from query-key similarity, but outputs are weighted sums of values.
Projection MatrixA projection matrix is a learned linear map that transforms vectors into another representation space. In Transformers, separate projection matrices create Q, K, and V from hidden states, and another projection maps concatenated head outputs back to the model dimension.
Position-wise MLPA position-wise MLP is the feed-forward sublayer in a Transformer block, applied independently to each token after attention. It adds nonlinearity and channel mixing per token, complementing attention, which mixes information across positions.
Residual Connection (Skip Connection)A residual connection adds a layer's input back to its output, so the layer learns a correction rather than an entirely new representation. This stabilizes optimization, improves gradient flow, and is one reason very deep networks and Transformers train reliably.
Root Mean Square Normalization (RMSNorm)RMSNorm normalizes activations by their root mean square without subtracting the mean. Compared with LayerNorm it is slightly cheaper and often just as effective, which is why many modern LLMs use RMSNorm in place of full mean-and-variance normalization.
Context WindowThe context window is the maximum number of tokens a model can process in one forward pass. It defines the model's accessible working memory at inference time, and longer windows increase both usefulness on long documents and computational cost.
AutoregressionAutoregression is the factorization of a sequence distribution into a product of conditional next-step distributions. In language generation it means producing one token at a time, each conditioned on all previously generated tokens.
PromptA prompt is the text or structured input given to a language model to condition its behavior and output. It can provide instructions, examples, retrieved context, or tool schemas, and in practice it acts as the model's temporary task specification.
System PromptA system prompt is a high-priority instruction block that defines the assistant's role, rules, and behavioral constraints for a conversation. It is usually prepended invisibly to user messages and is intended to override lower-priority prompt content.
Prompting FormatPrompting format is the template used to serialize instructions, roles, examples, and conversation turns into the token sequence a model expects. It matters because the same words in a different format can change model behavior, especially for chat-tuned systems.
Few-Shot PromptingFew-shot prompting includes a small number of labeled examples in the prompt so the model can infer the task from context without updating parameters. It is one of the clearest demonstrations of in-context learning in large language models.
In-Context LearningIn-context learning is the ability of a model to adapt its behavior from instructions or examples placed in the prompt, without changing its weights. The model remains frozen; the adaptation happens within the forward pass through pattern recognition over the context.
Prompt EngineeringPrompt engineering is the practice of designing prompts that make a model reliably produce the desired behavior. It includes choosing instructions, examples, structure, and reasoning scaffolds, and it trades parameter updates for careful interface design.
Chain of ThoughtChain of thought is a prompting strategy that elicits intermediate reasoning steps before the final answer. It often improves performance on multi-step tasks because the model can use the generated text as an external scratchpad rather than compressing all reasoning into one token prediction.
Tree of ThoughtTree of Thought extends chain-of-thought by exploring multiple candidate reasoning paths, evaluating intermediate states, and searching over them with strategies such as BFS or DFS. It is useful when solving the task requires branching, backtracking, or comparing alternative partial plans.
Self-ConsistencySelf-consistency samples multiple reasoning traces for the same problem and chooses the most common final answer rather than trusting a single chain of thought. It often boosts accuracy because different samples make different mistakes, while the correct answer tends to recur.
ReAct (Reason + Act)ReAct is a prompting pattern where a model alternates between reasoning in text and taking actions such as search or tool calls. This lets it use external information and observations to update its plan instead of reasoning only from the original prompt.
Function CallingFunction calling is a language-model capability for producing structured tool invocations instead of only plain text. The model selects a function and arguments that match a schema, which makes tool use more reliable and easier to integrate with software systems.
Large Language Model (LLM)A large language model is a very large neural language model, usually with billions of parameters, pretrained on massive text corpora. Scale gives LLMs broad world knowledge and emergent capabilities such as in-context learning, but the core training objective is still language modeling.
PretrainingPretraining is the large-scale first stage of training where a model learns general-purpose representations from unlabeled or self-supervised data. For LLMs this usually means next-token prediction over massive corpora, producing a base model that later fine-tuning can adapt.
Supervised Fine-Tuning (SFT)Supervised fine-tuning trains a pretrained model on curated input-output pairs so it follows instructions, styles, or task formats more reliably. In chat systems, SFT is the stage that turns a raw completion model into an assistant before preference alignment is applied.
FinetuningFinetuning continues training a pretrained model on a smaller task-specific or domain-specific dataset. It adapts existing representations rather than learning from scratch, which is why it usually needs far less data and compute than pretraining.
Full Fine-TuneA full fine-tune updates all of a model's parameters on the new task or domain. It offers maximum flexibility, but it is much more memory- and compute-intensive than PEFT methods and produces a separate full checkpoint for each adapted model.
Parameter-Efficient Fine-Tuning (PEFT)PEFT is a family of fine-tuning methods that keep most pretrained weights frozen and train only a small number of added or selected parameters. It preserves much of full fine-tuning's quality while reducing memory, compute, and storage costs.
Low-Rank Adaptation (LoRA)LoRA fine-tunes a model by expressing each weight update as a low-rank product \( \Delta W = BA \) while keeping the original weight matrix frozen. This dramatically cuts trainable parameters and optimizer state, which is why LoRA became the default PEFT method for LLMs.
LoRA AdapterA LoRA adapter is the task-specific pair of low-rank matrices inserted around a frozen base weight matrix to produce a learned update at inference or training time. Because adapters are small, many tasks can be stored, swapped, and merged without copying the full base model.
QLoRA (Quantized LoRA)QLoRA combines 4-bit quantization of the frozen base model with LoRA adapters trained in higher precision. This makes fine-tuning very large models feasible on modest hardware because the base weights stay compressed while only the small adapter parameters receive gradient updates.
Base ModelA base model is the pretrained model before instruction tuning, chat alignment, or task-specific fine-tuning. It is usually optimized only for language modeling, so it can complete text well but may not reliably follow user instructions or safety constraints.
Open-Weight ModelAn open-weight model is a model whose trained weights are publicly released for download and local use. That is more specific than 'open source': the weights may be open even when the training data, code, or full recipe are not.
Sampling (in Language Models)Sampling in language models means selecting the next token from the predicted probability distribution instead of always taking the argmax. The decoding rule strongly shapes diversity, coherence, and repetition, which is why temperature, top-k, and top-p matter so much.
Greedy DecodingGreedy decoding always selects the highest-probability next token at each step. It is simple and deterministic, but it often gets trapped in bland or repetitive continuations because it never explores slightly less probable alternatives that might lead to better sequences.
Top-k SamplingTop-k sampling truncates the next-token distribution to the \( k \) most probable tokens, renormalizes, and samples from that set. It removes the low-probability tail that often contains junk while still allowing controlled randomness.
Top-p Sampling (Nucleus Sampling)Top-p, or nucleus, sampling chooses the smallest set of tokens whose cumulative probability exceeds a threshold \( p \), then samples from that adaptive set. Unlike top-k, it expands when the model is uncertain and shrinks when the distribution is sharp.
Frequency PenaltyA frequency penalty subtracts an amount proportional to how many times a token has already appeared, lowering its future logit more with each repetition. It encourages lexical diversity without banning reuse entirely, which makes it gentler than hard repetition constraints.
Presence PenaltyA presence penalty lowers the score of any token that has already appeared, encouraging the model to introduce new words or topics instead of repeating earlier ones. Unlike a frequency penalty, it depends only on whether the token has appeared at least once, not on how many times it appeared.
HallucinationHallucination is when a model produces content that is unsupported or false while presenting it as if it were correct. In language models it often comes from next-token training, weak grounding, or overconfident decoding rather than deliberate deception.
MisalignmentMisalignment is the failure mode where optimizing a model for its training objective or proxy reward does not produce the behavior humans actually want. It includes problems like reward hacking, unsafe shortcuts, and goal pursuit that diverges from the intended specification.
Bias (Fairness)In fairness contexts, bias means systematic differences in treatment or error rates across groups caused by data, labels, measurement, or deployment choices. Fairness asks which notion of equal treatment matters, and different fairness criteria often cannot all be satisfied at once.
ExplainabilityExplainability is the ability to give a human-understandable reason for a model’s prediction or behavior using features, examples, rules, or mechanisms. A good explanation should be useful to a person and, ideally, faithful to what the model actually used.
Retrieval-Augmented Generation (RAG)Retrieval-augmented generation adds a retrieval step so the model conditions on external documents at inference time instead of relying only on memorized parameters. It can improve freshness and grounding, but answer quality depends heavily on retrieval recall, ranking, chunking, and how well the model uses the retrieved evidence.
Semantic SearchSemantic search retrieves results by meaning rather than exact keyword overlap, usually by embedding queries and documents into a vector space and comparing similarity. It handles paraphrases well, but it is often combined with lexical search when exact terms or identifiers matter.
Sparse Mixture-of-Experts (MoE) LayerA sparse mixture-of-experts layer replaces one dense feed-forward block with many expert subnetworks, but routes each token to only a small subset such as top-1 or top-2 experts. This increases parameter count and specialization without increasing per-token compute proportionally.
Router NetworkA router network scores experts or computation paths for each token and decides where that token should be sent in a conditional-compute model such as an MoE. A good router improves specialization while avoiding collapsed routing, overload, and excessive communication.
Expert NetworkAn expert network is one of the specialized submodules inside an MoE layer that processes only the tokens routed to it. Experts usually share the same architecture but learn different functions, so specialization emerges from routing plus load-balancing constraints.
Top-k RoutingTop-k routing sends each token only to the k highest-scoring experts instead of to every expert. This makes MoE computation sparse and efficient, but the choice of k trades off compute cost, robustness, and routing stability.
Load Balancing (MoE)Load balancing in MoE training adds losses or routing constraints so tokens are spread across experts instead of collapsing onto a few popular ones. It matters because uneven routing wastes capacity, creates bottlenecks, and leaves underused experts poorly trained.
Switch TransformerSwitch Transformer is a simplified MoE Transformer that routes each token to exactly one expert in each sparse feed-forward layer. Top-1 routing reduces communication and implementation complexity, enabling very large sparse models, but makes router stability and load balancing especially important.
Model MergingModel merging combines the weights or weight deltas of separately fine-tuned models into one model without full retraining. It can create multitask behavior cheaply, but naive averaging often causes interference unless the merged models share a common base and compatible parameter geometry.
Model SoupsModel soups average the weights of multiple fine-tuned models that lie in the same low-loss basin, often improving accuracy and robustness without extra inference cost. Unlike an ensemble, a soup is still a single model at serving time.
SLERP (Spherical Linear Interpolation)SLERP interpolates between two parameter vectors along the great-circle path on a sphere instead of using straight-line interpolation. In model merging it can preserve vector norms and sometimes produce smoother blends than linear interpolation when the two directions differ strongly.
TIES-MergingTIES-Merging is a model-merging method that reduces interference by trimming small parameter changes, resolving sign conflicts, and merging only updates aligned with an agreed sign. It is designed for cases where separately fine-tuned models disagree on which weights should move and in what direction.
DARE (Drop And REscale)DARE sparsifies fine-tuning deltas by randomly dropping many update entries and rescaling the rest, preserving most behavior while greatly reducing storage and merge interference. It is commonly used as a preprocessing step for delta compression or model merging rather than as a standalone training method.
Task VectorA task vector is the weight difference between a pre-trained model and the same model after fine-tuning on a task. Adding, subtracting, or scaling that vector can steer behavior, so task vectors provide a simple weight-space tool for editing or combining capabilities.
Model CompressionModel compression reduces a model’s memory, latency, or energy cost while trying to preserve performance. Common compression methods include distillation, pruning, quantization, low-rank factorization, and architecture redesign.
Knowledge DistillationKnowledge distillation trains a smaller student model to match the outputs or internal representations of a larger teacher. It transfers some of the teacher’s behavior into a cheaper model, often using soft targets that contain more information than hard labels alone.
PruningPruning removes weights, neurons, heads, or entire blocks that contribute little to performance. It can reduce model size or compute, but aggressive pruning usually needs fine-tuning to recover lost accuracy.
Structured PruningStructured pruning removes whole channels, heads, layers, or blocks, producing regular sparsity that hardware can exploit directly. It usually yields better real-world speedups than unstructured pruning, though it gives less fine-grained control.
Unstructured PruningUnstructured pruning zeros individual weights wherever they appear unimportant, creating irregular sparsity patterns. It can achieve high compression ratios, but specialized kernels are often needed to turn that sparsity into real latency gains.
QuantizationQuantization stores or computes with lower-precision numbers, such as INT8 or 4-bit values, instead of full-precision floats. It reduces memory bandwidth and can speed inference, but accuracy depends on how well the lower-precision representation preserves weights and activations.
Post-Training Quantization (PTQ)Post-training quantization converts a trained model to lower precision after optimization is finished, usually using a calibration set to estimate activation ranges. It is easy and cheap to apply, but accuracy can drop more than with quantization-aware training at very low bit widths.
Quantization-Aware Training (QAT)Quantization-aware training simulates low-precision arithmetic during training so the model learns weights that remain accurate after quantization. It usually outperforms post-training quantization at low bit widths, but it adds training cost and implementation complexity.
Preference-Based AlignmentPreference-based alignment trains models from judgments such as ‘response A is better than response B’ instead of only from supervised targets. It is useful when desired behavior is easier for humans to compare than to specify as a single correct answer.
Reinforcement Learning from Human Feedback (RLHF)RLHF aligns a model by collecting human preference data, training a reward model on those comparisons, and then optimizing the policy to maximize reward while staying close to a reference model. It improved helpfulness and instruction following, but it can also create reward hacking and training instability.
Reward ModelA reward model predicts a scalar preference score for a candidate response, usually from pairwise human comparisons. In RLHF it acts as a learned proxy objective, so the policy can exploit its mistakes if optimization pushes too hard against it.
Self-CritiqueSelf-critique is a prompting or training pattern where a model reviews its own draft, identifies problems, and then revises the answer. It can improve reasoning and safety, but only when the model can recognize errors more reliably than it makes them.
Constitutional AIConstitutional AI aligns a model using an explicit list of principles that guide critique and revision, reducing the need for dense human feedback on every example. The constitution acts like a rule set for self-improvement, though the resulting behavior still depends on the chosen principles and training procedure.
Direct Preference Optimization (DPO)DPO learns directly from preference pairs by making chosen responses more likely than rejected ones without running a separate RL loop. It can be derived from a KL-constrained reward-maximization view, which is why it is often presented as a simpler alternative to PPO-based RLHF.
Bradley-Terry ModelThe Bradley-Terry model turns pairwise comparisons into latent scores by assuming the probability that item A beats item B depends on their score difference. It is widely used for preference modeling, ranking, and reward-model training from pairwise judgments.
Pairwise ComparisonA pairwise comparison asks which of two items is better instead of assigning each item an absolute score. These judgments are often easier and more consistent for humans, which is why they are common in ranking, Elo-style systems, and alignment datasets.
RankingRanking is the task of ordering items by relevance, preference, or utility rather than predicting a single class label. It appears in search, recommendation, and alignment because the main question is which outputs should be placed above others.
Elo RatingElo rating estimates skill from pairwise wins and losses by updating each participant’s score based on expected versus actual outcomes. It was designed for chess, but the same logic is used to aggregate model preferences and benchmark head-to-head evaluations.
Pairwise RankingPairwise ranking learns an ordering from relative preferences between pairs rather than from absolute target values. Many ranking losses optimize the probability that preferred items score above rejected ones, which fits search and alignment data naturally.
Bootstrap ResamplingBootstrap resampling estimates uncertainty by repeatedly sampling with replacement from an observed dataset and recomputing a statistic on each resample. It is useful when analytic uncertainty formulas are hard to derive, though it assumes the sample is reasonably representative.
Confidence IntervalA confidence interval is a range produced by a procedure that would contain the true parameter a fixed fraction of the time over repeated samples, such as 95%. It quantifies estimation uncertainty, but it is not the probability that the parameter lies in this particular realized interval.
Vision Language Model (VLM)A vision-language model jointly processes images and text so it can describe, answer questions about, or reason across both modalities. Most VLMs combine a vision encoder with a language model through projection layers, cross-attention, or joint multimodal pretraining.
Cross-AttentionCross-attention lets one sequence or modality attend to representations produced by another sequence or modality. In encoder-decoder models the decoder queries encoder states, and in multimodal models text tokens often query visual features the same way.
Vision EncoderA vision encoder maps an image into features or tokens that downstream modules can use for classification, retrieval, or generation. CNNs and Vision Transformers are common vision encoders, differing mainly in how they represent spatial structure.
CLIP (Contrastive Language-Image Pre-training)CLIP learns a shared embedding space for images and text by pulling matched image-caption pairs together and pushing mismatched pairs apart. This contrastive objective enables zero-shot classification by comparing an image embedding against text prompts for candidate labels.
Program-Aided Language ModelA program-aided language model uses the LLM to translate a problem into executable code, then lets an interpreter carry out the exact computation. This separates natural-language understanding from symbolic execution and often improves arithmetic or algorithmic reasoning over pure chain-of-thought.
FlashAttentionFlashAttention is an exact attention algorithm that uses tiling and kernel fusion to minimize reads and writes between GPU HBM and on-chip SRAM. It preserves standard attention outputs while greatly reducing memory traffic, which yields large speed and memory gains on long sequences.
Data ParallelismData parallelism replicates the model on multiple devices and splits each batch across them, synchronizing gradients after each step. It is the simplest way to scale training throughput, but every device still stores the full model unless sharding is added.
Model ParallelismModel parallelism splits one model across multiple devices because it is too large or compute-heavy for a single device. The split can happen by layers, tensors, experts, or sequence chunks, trading memory savings for extra communication.
Pipeline ParallelismPipeline parallelism partitions a model by layers across devices and sends microbatches through the partitions like an assembly line. It reduces per-device memory, but pipeline bubbles and stage imbalance can waste throughput if the schedule is poorly tuned.
Tensor ParallelismTensor parallelism shards individual large matrix operations across devices, such as splitting weight matrices by rows or columns. It is effective for very large Transformers, but the frequent collectives mean fast interconnects are important.
Context ParallelismContext parallelism distributes a long sequence across devices so context tokens and their attention-related work are sharded instead of fully replicated. It helps long-context training or inference scale beyond one device, but requires extra communication to preserve exact attention across chunks.
Fully Sharded Data Parallel (FSDP)Fully Sharded Data Parallel shards model parameters, gradients, and optimizer states across data-parallel workers, gathering full parameters only when needed for computation. It is the PyTorch analogue of ZeRO-style training and makes much larger models fit without custom model-parallel code.
Model ShardingModel sharding splits a model’s parameters across devices or storage tiers instead of keeping a full copy everywhere. It is a general systems technique used in tensor parallelism, FSDP, offloading, and large-model serving to reduce per-device memory requirements.
Floating-Point Operations (FLOPs)FLOPs count the number of floating-point arithmetic operations required by a model or workload. They are a useful compute proxy for comparing training or inference cost, though real speed also depends on memory traffic, parallelism, and hardware utilization.
Mixed Precision TrainingMixed-precision training performs most computation in lower precision such as FP16 or bfloat16 while keeping selected quantities in higher precision for stability. It reduces memory use and often increases throughput without much accuracy loss when implemented carefully.
Long-Context PretrainingLong-context pretraining trains or continues training a model on examples with much longer sequences so it learns to use distant context instead of only fitting short windows. It is usually needed because simply changing positional scaling or the context limit does not teach robust long-range retrieval or reasoning.
Needle in a HaystackNeedle in a Haystack is a long-context benchmark that tests whether a model can retrieve a small target fact embedded inside a large distractor context. It is useful for measuring position-sensitive retrieval, but strong needle scores do not guarantee broader long-document reasoning.
Positional EncodingPositional encoding injects token order information into architectures like Transformers whose attention is otherwise permutation-invariant. It can be absolute or relative, and the choice strongly affects extrapolation, long-context behavior, and inductive bias.
Absolute Position EncodingAbsolute position encoding assigns each sequence position its own encoding or embedding and combines it with token representations. It works well inside the trained context range, but it often extrapolates poorly because positions are treated as fixed IDs rather than relative distances.
Relative Position EncodingRelative position encoding represents how far apart tokens are rather than assigning each position a standalone ID. That lets attention depend on distance or offset, which often improves length generalization and transfers patterns more naturally across positions.
Token IDA token ID is the integer index assigned to a token after tokenization. Models do not operate on raw text directly; they look up embeddings from token IDs and later map output logits back to IDs during decoding.
Vocabulary SizeVocabulary size is the number of distinct tokens a tokenizer can emit. A larger vocabulary shortens sequences but increases embedding and softmax size, while a smaller vocabulary produces longer sequences and more token fragmentation.
Subword TokenizationSubword tokenization splits text into frequent pieces smaller than words but larger than individual characters. It handles rare words and open vocabularies well by composing unfamiliar words from known subword units.
Special TokensSpecial tokens are reserved tokens with structural or control meaning, such as BOS, EOS, PAD, SEP, or mask tokens, rather than ordinary text content. They shape formatting, training objectives, and sometimes model behavior.
Padding TokenA padding token is a dummy token added so sequences in a batch have equal length. It should be ignored by the loss and usually masked from attention so it does not behave like real context.
BOS Token (Beginning of Sequence)A BOS token marks the beginning of a sequence and gives the model a consistent start symbol for conditioning generation or encoding. It can help define sequence boundaries and sometimes carries special training semantics.
EOS Token (End of Sequence)An EOS token marks the end of a sequence and tells the model where generation should stop. During training it teaches sequence termination, and during inference it is one of the main stopping conditions.
Attention MechanismAttention computes a context-dependent weighted combination of values, where the weights come from similarities between queries and keys. It lets a model focus on the most relevant parts of an input instead of compressing everything into one fixed vector.
Encoder-Decoder ArchitectureAn encoder-decoder architecture uses an encoder to turn an input sequence into representations and a decoder to generate an output sequence conditioned on those representations. It is the standard design for translation, summarization, and other input-to-output generation tasks.
Sequence-to-Sequence (Seq2Seq)Sequence-to-sequence learning maps one sequence to another, often with different lengths, such as translation or summarization. Modern seq2seq models are usually encoder-decoder Transformers, though earlier versions used recurrent networks with attention.
Distributed RepresentationA distributed representation stores a concept as a pattern across many features or neurons rather than in a single symbolic slot. This supports similarity, composition, and generalization because related concepts can occupy nearby regions of representation space.
Representation LearningRepresentation learning is the process of learning useful features automatically from data rather than hand-engineering them. Good representations preserve the structure that downstream tasks need, such as semantic similarity, invariances, or factors of variation.
Latent SpaceA latent space is the internal feature space in which a model represents inputs after transformation, often in a form that is more compact or task-relevant than raw data. Distances or directions in latent space can encode meaningful variation, but only relative to the model and objective that learned it.
Embedding SpaceAn embedding space is the vector space produced by an embedding model, where tokens, sentences, images, or other objects are mapped to dense numerical representations. Similarity in that space is used for retrieval, clustering, and transfer, though the geometry depends on the training objective.
Nearest Neighbor SearchNearest neighbor search finds the stored vectors most similar to a query under a chosen distance or similarity metric. Exact search is simple but expensive at scale, so large systems often use approximate nearest neighbor indexes instead.
Vector DatabaseA vector database is a system optimized for storing embeddings and retrieving nearest neighbors together with metadata filtering, updates, and persistence. It is the common serving layer behind semantic search and many RAG systems.
BM25BM25 is a sparse retrieval scoring function that ranks documents using term matches weighted by inverse document frequency and document-length normalization. It remains strong for exact lexical search and is often combined with dense retrieval in hybrid systems.
Sparse RetrievalSparse retrieval represents queries and documents with sparse term-based features such as inverted indexes, TF-IDF, or BM25. It excels at exact keywords and rare identifiers, but is weaker than dense retrieval on paraphrases and semantic matching.
Dense RetrievalDense retrieval represents queries and documents with learned dense embeddings and retrieves by vector similarity. It handles paraphrase and semantic matching better than sparse retrieval, but it can miss exact lexical constraints and usually relies on approximate nearest neighbor search.
GroundingGrounding means tying a model’s answer to external evidence, inputs, or world state rather than letting it generate from unsupported priors alone. In RAG or tool-use systems, grounding is what makes outputs traceable to retrieved context or observations.
FactualityFactuality is whether the content of an answer is actually true in the world or according to trusted sources. An answer can be fluent and even faithful to its source while still being nonfactual if the source itself is wrong or outdated.
FaithfulnessFaithfulness is whether a model’s output is supported by the provided input, source document, or chain of evidence. It differs from factuality because a summary can be perfectly faithful to a source that contains false claims.
CalibrationCalibration measures whether predicted probabilities match observed frequencies, so events predicted at 70% should occur about 70% of the time. A model can be accurate but poorly calibrated if its confidence is systematically too high or too low.
Temperature ScalingTemperature scaling calibrates a classifier by dividing logits by a learned scalar temperature before the softmax. It often improves probability calibration on a validation set without changing the model’s ranking of classes.
Batch NormalizationBatch normalization normalizes activations using mini-batch mean and variance, then applies learned scale and shift parameters. It stabilizes optimization and enables deeper networks, but its behavior differs between training and inference because it relies on running statistics.
Layer NormalizationLayer normalization normalizes activations across features within each example rather than across the batch. It works well for variable-length sequences and small batch sizes, which is why it is standard in Transformers.
Gradient ClippingGradient clipping limits gradient norms or values before the optimizer step to prevent unstable updates and exploding gradients. It does not fix a bad objective, but it can stabilize training when rare large gradients would otherwise dominate.
Weight DecayWeight decay shrinks parameters toward zero by multiplying them by a factor slightly below 1 on each optimizer step. In plain SGD it is equivalent to L2 regularization, but in adaptive optimizers the decoupled AdamW form is usually preferred.
Adam OptimizerAdam is an adaptive first-order optimizer that keeps moving averages of the gradient and its square, then bias-corrects them to scale each parameter’s update. It converges quickly and is standard for Transformer training, though it is sensitive to weight decay design and hyperparameters.
AdaGradAdaGrad adapts learning rates by dividing each parameter’s update by the square root of the accumulated historical squared gradients. It works especially well for sparse features, but its learning rates can decay too aggressively over long training runs.
RMSPropRMSProp uses an exponential moving average of squared gradients to normalize updates, preventing the denominator from growing without bound as in AdaGrad. It is useful for nonstationary problems and was a key precursor to Adam.
MomentumMomentum accumulates a running velocity of past gradients so updates keep moving in consistent directions and damp noisy zig-zags. It speeds optimization in ravines and is commonly paired with SGD or Nesterov variants.
Learning Rate ScheduleA learning-rate schedule changes the learning rate over training instead of keeping it constant. Schedules matter because they balance fast early progress with stable late optimization and often determine final performance as much as the base optimizer.
WarmupWarmup starts training with a small learning rate and gradually increases it during the first steps. It reduces early instability, especially in Transformers where large updates before optimizer statistics settle can cause divergence.
Cosine AnnealingCosine annealing decays the learning rate following a cosine curve from a high value to a low value, sometimes with restarts. It provides a smooth schedule that often works well in practice without needing many hand-tuned decay boundaries.
Weight InitializationWeight initialization chooses starting parameter values before training begins. Good initialization keeps activations and gradients in useful ranges so learning can start without vanishing, exploding, or breaking symmetry.
He Initialization (Kaiming Initialization)He initialization sets weight variance to roughly 2/fan-in so ReLU-like activations preserve signal magnitude through depth. It improves on Xavier initialization for one-sided activations that zero out about half the inputs.
Label SmoothingLabel smoothing replaces hard one-hot targets with a mostly-correct probability distribution that assigns a small amount of mass to other classes. It regularizes overconfident classifiers and often improves generalization and calibration, though it can hurt when exact probabilities matter.
Tokenization PipelineA tokenization pipeline is the full process that turns raw text into model-ready inputs, including normalization, pre-tokenization, subword splitting, token-to-ID mapping, truncation, padding, and special-token insertion. Choices here directly affect sequence length, vocabulary coverage, and downstream behavior.
GPU AccelerationGPU acceleration uses highly parallel graphics processors to speed the matrix and tensor operations that dominate modern ML workloads. It matters because deep learning is mostly throughput-bound linear algebra, which GPUs execute far more efficiently than general-purpose CPUs.
CUDACUDA is NVIDIA’s parallel-computing platform and programming model for running general-purpose kernels on GPUs. In machine learning it is the software layer that makes GPU-accelerated training and inference practical, exposing massive parallelism, specialized libraries, and direct control over device memory.
Inference OptimizationInference optimization is the set of techniques that reduce serving latency, memory use, and cost while preserving acceptable quality. Common methods include quantization, batching, KV-cache optimizations, kernel fusion, speculative decoding, and architecture choices that trade a little flexibility for much higher throughput.
Instruction TuningInstruction tuning is supervised fine-tuning on instruction-response examples so a pretrained model learns to follow requests instead of merely continuing text. It improves task generality and usability, but it mainly changes behavior and format-following rather than adding much new world knowledge.
Safety AlignmentSafety alignment is the process of making a model reliably avoid harmful, deceptive, or policy-violating behavior while remaining useful. In practice it combines data curation, supervised tuning, preference optimization or RLHF, classifiers, and adversarial evaluation, but it never guarantees perfect safety.
Red-TeamingRed-teaming is adversarial evaluation in which people or automated systems deliberately try to break a model’s safeguards and expose failure modes. Its purpose is not to improve benchmark scores directly, but to find unsafe or brittle behavior before deployment.
What is a jailbreak in the context of LLMs?In the context of LLMs, a jailbreak is a prompt or interaction pattern that bypasses the model’s safety training or policy enforcement and elicits behavior it was supposed to refuse. Jailbreaks matter because they reveal that aligned behavior can be a thin behavioral layer rather than a deep guarantee.
Adversarial PromptingAdversarial prompting is the deliberate construction of inputs that push a model toward incorrect, unsafe, or unintended behavior. It includes jailbreaks, prompt injection, data exfiltration attempts, and other attacks that exploit weaknesses in instruction-following or context handling.
Benchmark (ML Evaluation)A benchmark in ML evaluation is a standardized task, dataset, metric, and protocol used to compare systems reproducibly. Benchmarks are useful because they make progress measurable, but they can be gamed, saturated, or misaligned with real-world performance.
Human EvaluationHuman evaluation uses people to judge outputs on qualities such as helpfulness, factuality, coherence, or safety that automated metrics often miss. It is usually the most trustworthy evaluation for subjective tasks, but it is expensive, slow, and sensitive to rubric design and annotator variance.
Automated EvaluationAutomated evaluation scores model outputs with metrics or model-based judges instead of human raters. It is fast, scalable, and reproducible, but its usefulness depends on how well the metric correlates with the human judgment that actually matters.
BLEUBLEU is a machine-translation metric based mainly on n-gram precision against one or more reference texts, combined with a brevity penalty. It is useful for corpus-level comparison, but it often misses meaning-preserving paraphrases and is weak as a sentence-level quality measure.
A/B Testing (ML Systems)A/B testing in ML systems is a randomized online experiment that serves different model variants to different user groups and compares outcome metrics. It is the standard way to measure real production impact, because offline wins do not always translate into better user experience.
Continual LearningContinual learning is the problem of learning from a sequence of tasks or data distributions without losing previously acquired capabilities. Its core challenge is the stability-plasticity tradeoff: the model must remain adaptable to new data without catastrophically overwriting old knowledge.
What is catastrophic forgetting?Catastrophic forgetting is the sharp loss of performance on previously learned tasks after a model is trained on new ones. It happens because gradient updates that help the new task can overwrite internal representations that were supporting the old task.
Curriculum LearningCurriculum learning trains a model on examples in an organized order, usually from easier or more structured cases to harder ones. The idea is to improve optimization and generalization by shaping the training distribution, though a bad curriculum can also slow learning or bias the model.
Pretraining CorpusA pretraining corpus is the large unlabeled dataset used to train a model’s base capabilities through self-supervised objectives such as next-token prediction. Its size, quality, duplication rate, domain mix, and filtering choices strongly shape what the model knows and how it behaves.
Instruction DatasetAn instruction dataset is a curated set of prompts paired with preferred responses used to teach a pretrained model how to behave as an assistant. It mainly teaches task framing, format, and interaction style rather than the broad world model that comes from pretraining.
Preference DatasetA preference dataset contains prompts with ranked, paired, or binary-labeled responses indicating which outputs are preferred. It is the standard supervision source for reward modeling and direct preference objectives because it expresses comparative quality better than a single gold answer.
Reward SignalA reward signal is the scalar feedback an RL agent receives about the desirability of its behavior. Because the agent optimizes whatever reward it is given, the design of the reward signal determines whether learning produces the intended behavior or merely exploits a proxy.
Policy (Reinforcement Learning)In reinforcement learning, a policy is the rule that maps states or observations to actions, often as a probability distribution. Learning a policy means directly improving behavior, and in language-model RL the policy is the model’s distribution over tokens or completions conditioned on context.
Value FunctionA value function estimates expected future return, either from a state or from a state-action pair under a policy. It matters because it turns delayed rewards into local training signals, enabling planning, bootstrapping, and lower-variance policy gradients.
AI SafetyAI safety is the broader field concerned with preventing harmful or catastrophic outcomes from advanced AI systems. It includes alignment, robustness, misuse prevention, monitoring, control, and governance, so it is wider than just making a chatbot refuse bad requests.
Alignment (AI)Alignment in AI is the problem of making an AI system’s objectives and behavior match human intentions and values rather than a flawed proxy. The hard part is not only teaching what humans say they want, but ensuring the system pursues that goal robustly in new situations.
InterpretabilityInterpretability is the study of making model behavior understandable to humans, whether by explaining predictions, revealing learned features, or analyzing internal structure. It matters because debugging, trust, scientific understanding, and safety all depend on seeing more than just inputs and outputs.
Mechanistic InterpretabilityMechanistic interpretability treats a neural network as a system to be reverse-engineered into circuits, features, and algorithms. Its goal is not just to correlate neurons with concepts, but to identify the actual internal computations that produce behavior.
Attention VisualizationAttention visualization renders attention weights as heatmaps or token-to-token graphs so we can see which positions a model attends to. It is a useful diagnostic tool, but attention weights alone are not a complete explanation of what the model is computing.
Probing (Neural Networks)Probing tests whether information is encoded in a model’s hidden states by training a simple classifier or regressor on those representations. A successful probe shows that the information is recoverable, but not necessarily that the model causally uses it.
Logit LensLogit Lens maps intermediate hidden states through the final unembedding matrix to inspect what tokens each layer already appears to favor. It is a convenient way to watch a Transformer’s computation unfold, though it is only approximate because earlier layers were not trained to be decoded directly.
Activation AnalysisActivation analysis studies the intermediate activations produced during a forward pass rather than only the model’s static weights. By examining which neurons, channels, or directions fire in different contexts, it helps connect internal representations to model behavior.
Sparse Autoencoder (Mechanistic Interpretability)In mechanistic interpretability, a sparse autoencoder is trained on model activations to decompose dense, superposed representations into a larger set of sparse features. This often makes latent structure more interpretable, because individual learned directions can line up with human-readable concepts or behaviors.
Superposition (Neural Networks)Superposition is the phenomenon in which a network stores more features than it has obvious dimensions by packing them into overlapping directions. It explains why single neurons can look polysemantic and why sparse feature dictionaries are often more informative than neuron-by-neuron inspection.
Foundation ModelA foundation model is a large general-purpose model pretrained on broad data and then adapted to many downstream uses through prompting, fine-tuning, or tool use. Its defining property is transfer: one base model can support many tasks rather than being built for just one.
Scaling LawsScaling laws are empirical relationships showing how loss or capability changes with model size, data, and compute, often following approximate power laws. They matter because they let researchers forecast returns to scale and choose more compute-efficient training regimes.
What are emergent capabilities in large language models?Emergent capabilities in large language models are abilities that look weak or absent at small scale but become strong once the model is large enough. The key caveat is that “emergence” can depend on the metric and threshold used, so apparent jumps are not always literal discontinuities in the underlying capability.
Zero-Shot LearningZero-shot learning is the ability to perform a task from a description alone, without task-specific training examples in the prompt or fine-tuning data. In LLMs it is a direct consequence of broad pretraining and instruction-following ability.
One-Shot LearningOne-shot learning is the ability to learn or generalize from a single labeled example or demonstration. It matters because many real tasks do not provide large datasets, so the model must infer the rule from minimal evidence.
Transfer LearningTransfer learning reuses knowledge learned on one task or dataset to improve performance on another. It is effective because useful features learned in a high-resource setting often remain useful in a lower-resource target domain.
Data AugmentationData augmentation expands a training set with label-preserving transformations such as crops, paraphrases, or noise injection. It improves generalization by teaching the model which variations should not change the answer.
Active LearningActive learning is a training strategy that selectively asks for labels on the most informative unlabeled examples instead of labeling data uniformly at random. Its purpose is to reduce annotation cost by spending human effort where uncertainty or disagreement is highest.
Classification HeadA classification head is the final task-specific layer that maps learned representations to class logits or probabilities. In transfer learning it is often the only part trained from scratch, while the backbone provides reusable features.
Softmax HeadA softmax head is the output projection plus softmax normalization that converts hidden representations into a probability distribution over classes or vocabulary items. In language models it is the layer that turns the final hidden state into next-token probabilities.
Beam SearchBeam search is a decoding algorithm that keeps the top-scoring partial sequences at each step instead of only the single best one. It approximates high-probability generation better than greedy decoding, but it can still miss the global optimum and often reduces diversity.
Repetition PenaltyA repetition penalty is a decoding heuristic that downweights tokens or phrases the model has already used, reducing loops and bland repetition. It improves generation quality when a model is prone to degeneracy, but too much penalty can make text unnatural or incoherent.
Encoder (Transformer)A Transformer encoder is a stack of self-attention and feed-forward blocks that builds contextual representations of an input sequence. Because encoder self-attention is usually bidirectional, it is well suited for understanding tasks such as classification, retrieval, and sequence labeling.
Decoder (Transformer)A Transformer decoder is the autoregressive half of the architecture that predicts tokens using causal self-attention and, in encoder-decoder models, optional cross-attention to an encoder output. Its defining constraint is that each position can attend only to earlier positions when generating.
Neural Language ModelA neural language model predicts text with learned distributed representations and a neural network rather than count tables. Its main advantage over classical n-gram models is that it can generalize to unseen contexts by sharing statistical strength across similar words and patterns.
Semantic SpaceA semantic space is an embedding space in which geometric relations reflect meaning, similarity, or functional role. Nearby points tend to correspond to semantically related items, which is why vector search and representation learning work at all.
Information RetrievalInformation retrieval is the problem of finding and ranking the documents, passages, or items most relevant to a query. Modern systems combine lexical matching, learned embeddings, and ranking models because exact term overlap and semantic similarity each capture different kinds of relevance.
Logit AdjustmentLogit adjustment means modifying logits to account for effects such as class imbalance, prior shift, or calibration goals before taking probabilities or losses. It changes the decision boundary in a simple way by shifting scores rather than changing the underlying representation.
Softmax NormalizationSoftmax normalization converts a vector of logits into a probability distribution by exponentiating each score and dividing by the total. It preserves rank order while making outputs comparable, which is why it is the standard final normalization for multiclass prediction.
Over-ParameterizationOver-parameterization means a model has far more parameters than the minimal number apparently needed to fit the data. Counterintuitively, this often helps optimization and can still generalize well because training dynamics and implicit regularization matter as much as raw parameter count.
Domain-Specific PretrainingDomain-specific pretraining continues or repeats pretraining on corpus data from a specialized domain such as law, medicine, or code. It improves vocabulary use, factual recall, and style in that domain, but it can also narrow the model or erode performance outside the target distribution.
Distributed Computing (ML Training)Distributed computing in ML training spreads computation, memory, or both across many devices and often many machines. It is what makes modern large-model training possible through strategies such as data parallelism, model parallelism, sharding, and pipeline execution.
Memory Optimization (ML Training)Memory optimization in ML training is the collection of techniques that reduce peak memory so larger models or batches fit on available hardware. Common examples are mixed precision, activation checkpointing, optimizer sharding, offloading, and more memory-efficient attention kernels.
Conversational AIConversational AI is a class of systems designed for multi-turn interaction, where the model must respond helpfully while tracking context, intent, and dialogue state. The hard part is not generating one good answer, but remaining coherent and useful across an extended interaction.
Role-Playing (LLMs)Role-playing in LLMs means conditioning a model to adopt a persona, voice, or behavioral frame during generation. It is useful for simulation and product design, but it also shows how easily high-level behavior can be steered by prompt context.
Model EvaluationModel evaluation is the systematic measurement of how a model performs, fails, and trades off across tasks, metrics, and deployment contexts. Good evaluation combines offline benchmarks, stress tests, human judgment, and online metrics rather than relying on a single score.
Shadow DeploymentShadow deployment runs a new model in production alongside the live system without letting its outputs affect users. This makes it possible to compare latency, quality, and failure modes on real traffic before committing to a risky rollout.
Feedback Loop (ML Systems)A feedback loop in an ML system occurs when the model’s outputs change the data it will later train on or be evaluated against. These loops can reinforce bias, distort demand, and make offline metrics look better even while the real system gets worse.
Data MixtureA data mixture is the weighting and composition of different datasets or domains in a training run. It matters because capability, robustness, and bias often depend as much on what proportion of the data comes from each source as on the total token count.
GRPO (Group Relative Policy Optimization)GRPO is a policy-optimization method that scores sampled responses relative to others in the same group, using those relative rewards to update the policy. Its appeal is that it can improve reasoning performance while avoiding some of the memory overhead of PPO-style critic training.
Bias MitigationBias mitigation is the set of methods used to reduce unfair or systematically skewed behavior in models and datasets. It can act before training, during optimization, or after deployment, but every intervention trades off fairness goals, accuracy, and operational complexity.
Transparency (AI Systems)Transparency in AI systems means making system behavior, limitations, provenance, and decision pathways inspectable to users, developers, or regulators. It is broader than interpretability because it includes documentation, reporting, and operational visibility, not just internal model analysis.
Steering VectorsSteering vectors are directions in activation space that, when added to hidden states, systematically change model behavior toward traits such as refusal, sentiment, or persona. They are useful because they show that some behaviors can be modified directly in representation space without full retraining.
Activation PatchingActivation patching is a causal analysis method where activations from one run are inserted into another to test which components matter for a given behavior. If patching a layer or head restores the behavior, that component is evidence for being on the relevant causal path.
Log-Sum-Exp TrickThe log-sum-exp trick computes expressions like log(sum(exp(x_i))) stably by subtracting the maximum logit before exponentiation. It prevents overflow and underflow, so it is a standard numerical tool in softmax, cross-entropy, and probabilistic inference.
Exponential Family of DistributionsThe exponential family is the class of distributions that can be written in the form exp(eta^T T(x) - A(eta) + c(x)). This shared form gives them sufficient statistics, convenient conjugate priors, and clean maximum-likelihood geometry, which is why they dominate classical statistical modeling.
Kolmogorov-Arnold NetworksKolmogorov-Arnold Networks replace fixed scalar weights on edges with learnable one-dimensional functions, so layers are built from sums of learned univariate transforms rather than simple affine maps. They are motivated by the Kolmogorov-Arnold representation theorem and are often discussed as a more interpretable alternative to MLPs, not a universal replacement.