Instruction Following

Large Language Model

A neural network trained on massive text corpora that can generate, understand, and reason about natural language.

GPT

Generative Pre-trained Transformer — a family of autoregressive language models that generate text by predicting the next token.

BERT

Bidirectional Encoder Representations from Transformers — a language model that understands context by reading text in both directions.

Tokenisation

The process of breaking text into smaller units (tokens) such as words, subwords, or characters for processing by language models.

Language Model

A probabilistic model that assigns probabilities to sequences of words, enabling prediction of the next word in a sequence.

Contextual Embedding

Word representations that change based on surrounding context, capturing polysemy and contextual meaning.

Word2Vec

A neural network model that learns distributed word representations by predicting surrounding context words.

GloVe

Global Vectors for Word Representation — an unsupervised learning algorithm for obtaining word vector representations from aggregated word co-occurrence statistics.

Instruction Tuning

Training a language model to follow natural language instructions by fine-tuning on instruction-response pairs.

RLHF

Reinforcement Learning from Human Feedback — a technique for aligning language models with human preferences through reward modelling.

Grounding

Connecting language model outputs to real-world knowledge, facts, or data sources to improve factual accuracy.

Hallucination Detection

Techniques for identifying when AI language models generate plausible but factually incorrect or unsupported content.

AI systems designed to engage in natural, context-aware dialogue with humans across multiple turns.

The maximum number of tokens a language model can process in a single input-output interaction.

AI systems that extract structured information from unstructured documents by combining optical character recognition, layout analysis, and natural language comprehension.

An NLP task where a system automatically answers questions posed in natural language based on given context.

A generative probabilistic model for discovering topics in a collection of documents.

The classification of user utterances into predefined categories representing the user's goal or purpose, a fundamental component of conversational AI and chatbot systems.

The field of AI focused on enabling computers to understand, interpret, and generate human language.

Techniques and architectures that enable language models to process and reason over extremely long input sequences, from tens of thousands to millions of tokens.