Tokenisation

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.

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.

Prompt Injection

A security vulnerability where malicious inputs manipulate a language model into ignoring its instructions or producing unintended outputs.

The use of AI to automatically translate text or speech from one natural language to another.

The task of identifying all expressions in text that refer to the same real-world entity.

A computer system designed to converse with humans, encompassing task-oriented and open-domain conversation.

A text summarisation approach that generates novel sentences to capture the essential meaning of a document, rather than simply extracting and rearranging existing sentences.

The subfield of NLP focused on machine reading comprehension and extracting meaning from text.

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

The component of conversational systems that tracks conversation state, determines the next system action, and maintains coherent multi-turn interactions with users.

A language model trained on text from dozens or hundreds of languages simultaneously, enabling cross-lingual understanding and generation without language-specific fine-tuning.