Long-Context Modelling

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.

A neural network architecture that maps an input sequence to an output sequence, used in translation and summarisation.

Identifying semantic relationships between entities mentioned in text.

An approach to AI alignment where models are trained to follow a set of principles or constitution.

Dense vector representations of text passages that capture semantic meaning for similarity comparison and retrieval.

A database optimised for storing and querying high-dimensional vector embeddings for similarity search.

The generation of machine-readable formatted responses such as JSON, XML, or code from language models, enabling reliable integration with downstream software systems.

A two-stage retrieval process where an initial set of candidate documents is rescored by a more powerful model to improve the relevance ordering of search results.

An unsupervised technique for discovering abstract topics that occur in a collection of documents.