Language Model

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.

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

An NLP task where a system automatically answers questions posed in natural language based on given context.

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

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

Identifying semantic relationships between entities mentioned in text.

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

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

AI systems designed to engage in natural, context-aware dialogue with humans across multiple turns.