Contextual Embedding

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.

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

Search technology that understands the meaning and intent behind queries rather than just matching keywords.

Technology that converts written text into natural-sounding spoken audio using neural networks, enabling voice interfaces, accessibility tools, and content narration.

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

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

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

A software application that simulates human conversation through text or voice interactions using NLP.

The subfield of NLP concerned with producing natural language text from structured data or representations.