LLM Glossary

This glossary provides definitions for key terms used in the field of Large Language Models. Terms are organized alphabetically within categories for easy reference.

Foundational Terms

A

Activation Function: A mathematical function applied to neuron outputs to introduce non-linearity. Common examples include ReLU, sigmoid, and tanh.

Attention Mechanism: A neural network component that computes weighted sums of input representations, allowing the model to focus on relevant parts of the input.

Attention Score: The weight assigned to a key-value pair in attention computation, determining how much focus to place on that part of the input.

Autoregressive Model: A model that generates outputs sequentially, where each output token depends on previously generated tokens.

B

Backpropagation: The algorithm for computing gradients in neural networks, used to update model parameters during training.

Beam Search: A decoding algorithm that explores multiple possible sequences simultaneously, keeping the top-k most probable sequences at each step.

BERT (Bidirectional Encoder Representations from Transformers): A transformer-based model that uses bidirectional context for natural language understanding tasks.

BPE (Byte-Pair Encoding): A tokenization algorithm that iteratively merges the most frequent character pairs to build a vocabulary.

Buffer Pool: Memory management structure for efficient attention computation in transformers.

C

Chain-of-Thought (CoT): A prompting technique that encourages models to show intermediate reasoning steps before providing final answers.

Chinchilla Scaling Law: A principle stating that model performance scales predictably with parameters, data, and compute, with optimal allocation requiring proportional scaling.

CLM (Causal Language Modeling): Language modeling that predicts the next token in a sequence, also known as autoregressive language modeling.

Context Window: The maximum number of tokens a model can process in a single forward pass.

Contrastive Learning: Training methods that learn representations by contrasting positive and negative examples.

Constitutional AI: An alignment approach where AI systems learn to follow principles or "constitutions" through self-critique and revision.

Cosine Similarity: A measure of similarity between two vectors, computed as the cosine of the angle between them.

D

Deduplication: The process of removing duplicate data points from training datasets.

Decoder: The part of a transformer that generates output sequences, typically used in autoregressive models.

Deep Learning: A subset of machine learning using neural networks with multiple layers to learn hierarchical representations.

Denoising Objective: A training objective that requires the model to reconstruct clean inputs from corrupted versions.

Diffusion Model: A generative model that learns to generate data by reversing a gradual noising process.

DPO (Direct Preference Optimization): A method for aligning language models with human preferences without reinforcement learning.

Dropout: A regularization technique that randomly sets a fraction of neurons to zero during training.

DSPy: A framework for programming with language models, emphasizing composable modules and automatic optimization.

E

Embedding: A dense vector representation of a discrete object (word, token, document) in a continuous vector space.

Encoder: The part of a transformer that processes input sequences into representations, typically used for understanding tasks.

Epoch: One complete pass through the entire training dataset.

Emergent Capabilities: Abilities that appear suddenly in large models but are absent in smaller models.

EOS Token (End of Sequence): A special token indicating the end of a sequence.

Eval Set: A dataset used to evaluate model performance during training.

F

Few-Shot Learning: Learning from a small number of examples provided in the prompt, without gradient updates.

Fine-Tuning: Further training a pre-trained model on task-specific data to adapt it to new tasks.

Flash Attention: An efficient attention implementation that reduces memory usage and improves speed.

Float16 (FP16): A 16-bit floating-point format used for efficient model training and inference.

FLOPs (Floating Point Operations): A measure of computational complexity, used to estimate training and inference costs.

Function Calling: The ability of LLMs to call external functions or APIs based on user requests.

G

GPT (Generative Pre-trained Transformer): An autoregressive language model architecture using transformer decoders.

Gradient Accumulation: A technique for simulating larger batch sizes by accumulating gradients over multiple forward passes.

Gradient Checkpointing: A memory optimization technique that recomputes activations during backpropagation instead of storing them.

Grounding: Connecting model outputs to verifiable facts or external sources.

Grouped-Query Attention (GQA): An attention variant where multiple query heads share key-value heads, reducing memory usage.

H

Hallucination: When a model generates information that is factually incorrect or not grounded in the input.

Hugging Face: A platform providing tools and resources for working with machine learning models, especially NLP.

Human Evaluation: Assessment of model outputs by human judges, considered the gold standard for quality evaluation.

HuggingFace Transformers: A library providing pre-trained models and tools for NLP tasks.

I

ICL (In-Context Learning): Learning from examples provided in the prompt without updating model parameters.

Instruction Tuning: Fine-tuning models on instruction-response pairs to improve instruction following.

Inference: The process of using a trained model to generate outputs from inputs.

Interpolation: Generating outputs that fall within the range of training data, as opposed to extrapolation.

J

Jailbreaking: Attempting to bypass model safety restrictions through adversarial prompts.

K

KVM (Key-Value Memory): Cached key-value pairs from attention layers, used to speed up autoregressive generation.

Knowledge Distillation: Training a smaller model to mimic a larger model's behavior.

Knowledge Graph: A structured representation of facts as entities and relationships.

L

Language Model: A probabilistic model of text that assigns probabilities to sequences of tokens.

Latent Space: The learned representation space where similar inputs are mapped to nearby points.

Layer Normalization: A normalization technique applied across features for each input sample.

LoRA (Low-Rank Adaptation): A parameter-efficient fine-tuning method that adds low-rank matrices to existing weights.

LLaMA (Large Language Model Meta AI): An open-source language model architecture from Meta.

LM Head: The output layer of a language model that projects hidden states to vocabulary probabilities.

M

Masked Language Modeling (MLM): A training objective where random tokens are masked and the model predicts them.

Machine Translation: Automatically translating text from one language to another using computational methods.

MMLU (Massive Multitask Language Understanding): A benchmark evaluating knowledge across 57 subjects.

Multi-Head Attention: Attention mechanism with multiple parallel attention heads capturing different patterns.

N

Named Entity Recognition (NER): Identifying and classifying named entities in text.

Neural Scaling Laws: Power-law relationships between model performance and factors like parameters, data, and compute.

NLL (Negative Log-Likelihood): A common loss function for training probabilistic models.

NLP (Natural Language Processing): The field of AI focused on understanding and generating human language.

Non-Parametric: Methods that don't learn fixed parameters but instead use memory or lookup tables.

O

OOD (Out-of-Distribution): Data that differs significantly from the training distribution.

Open-Source Model: A model whose weights and often training code are publicly available.

OpenAI: A research organization developing and deploying AI systems, including GPT models.

Outlier Detection: Identifying unusual data points that deviate significantly from normal patterns.

P

Packing: Combining multiple short sequences into a single longer sequence to improve training efficiency.

PaLM (Pathways Language Model): A large language model from Google using the Pathways system.

Parameters: Learnable weights in a neural network that are updated during training.

Perplexity: A measure of how well a probability model predicts a sample, computed as the exponentiated average negative log-likelihood.

Positional Encoding: Adding position information to tokens since transformers lack inherent sequence order.

Pre-training: Initial training on large datasets to learn general representations before fine-tuning.

Prompt Engineering: Designing input prompts to elicit desired outputs from language models.

Q

QLoRA: A quantized version of LoRA that uses 4-bit quantization for memory efficiency.

Quantization: Reducing the precision of model weights to reduce memory usage and improve inference speed.

R

RAG (Retrieval-Augmented Generation): Combining retrieval of relevant documents with generation for more accurate outputs.

Recall: The fraction of relevant items that are successfully retrieved or identified.

Reinforcement Learning from Human Feedback (RLHF): Training models to align with human preferences using reinforcement learning.

Replay Buffer: Stored experiences used for training in reinforcement learning.

Residual Connection: Skip connections that add input directly to layer output, enabling deeper networks.

S

Sampling: Generating tokens by randomly selecting from the probability distribution.

Scaling Law: Mathematical relationships describing how model performance improves with scale.

Self-Attention: Attention mechanism where queries, keys, and values all come from the same sequence.

Sentence Embedding: A vector representation of a sentence or paragraph.

Softmax: A function that converts logits to probabilities summing to 1.

Speculative Decoding: A technique that uses a smaller model to generate draft tokens verified by a larger model.

SFT (Supervised Fine-Tuning): Fine-tuning on labeled data with input-output pairs.

T

Temperature: A parameter controlling randomness in sampling; higher values increase diversity.

Tensor: A multi-dimensional array, the fundamental data structure in deep learning.

TF-IDF: Term Frequency-Inverse Document Frequency, a weighting scheme for importance.

Token: A unit of text, which can be a word, subword, or character.

Tokenization: Splitting text into tokens for model processing.

Top-p (Nucleus) Sampling: Sampling from the smallest set of tokens whose cumulative probability exceeds p.

Transformer: A neural network architecture based on self-attention, introduced in "Attention Is All You Need."

U

Unembedding: The reverse of embedding, mapping from vector space back to discrete tokens.

Unigram: A single token or word.

Unsupervised Learning: Learning patterns from unlabeled data.

V

VAE (Variational Autoencoder): A generative model that learns a latent space for data generation.

Vocabulary: The set of all tokens a model can process.

Vocabulary Size: The total number of unique tokens in the model's vocabulary.

W

Weight Decay: A regularization technique that penalizes large weights during training.

Word Embedding: A dense vector representing a word's meaning.

X

XLA (Accelerated Linear Algebra): An optimized linear algebra compiler for TensorFlow.

Z

Zero-Shot Learning: Performing tasks without any task-specific examples, relying solely on instructions.

Zipf's Law: The empirical observation that word frequency is inversely proportional to rank.

Evaluation Metrics

Abbreviations

Practice Exercises

1. Term Definition: Define the following terms in your own words: attention mechanism, transformer, fine-tuning, RLHF.

2. Term Relationships: How do the following terms relate to each other: GPT, transformer, autoregressive, language modeling?

3. Term Application: When would you use the following: RAG, LoRA, DPO, chain-of-thought prompting?

4. Term Evolution: How have the definitions of "large language model" and "alignment" evolved over time?

What to Learn Next