GloVe

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.

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.

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

The process of assigning grammatical categories (noun, verb, adjective) to each word in a text.

An NLP task that identifies and classifies named entities in text into categories like person, organisation, and location.

A fine-grained sentiment analysis approach that identifies opinions directed at specific aspects or features of an entity, such as a product's price, quality, or design.

The computational study of people's opinions, emotions, and attitudes expressed in text.

The maximum amount of text a language model can consider at once when generating a response.

The task of assigning predefined categories or labels to text documents based on their content.

A measure of how closely the meanings of two text passages align, computed through embedding comparison and used in duplicate detection, search, and recommendation systems.

Unsupervised Learning

A machine learning approach where models discover patterns and structures in data without labelled examples.