RLHF

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.

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.

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

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

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

The application of models trained in one language to perform tasks in another language, leveraging shared multilingual representations learned during pre-training.

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

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

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

The capability of language models to accurately interpret and execute natural language instructions, a core skill developed through instruction tuning and alignment training.

Reinforcement Learning

A machine learning paradigm where agents learn optimal behaviour through trial and error, receiving rewards or penalties.

Reinforcement Learning from Human Feedback

A training paradigm where AI models are refined using human preference signals, aligning model outputs with human values and quality expectations through reward modelling.