Structured Output

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.

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.

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

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

The classification of user utterances into predefined categories representing the user's goal or purpose, a fundamental component of conversational AI and chatbot systems.

A subword tokenisation algorithm that iteratively merges the most frequent character pairs to build a vocabulary.

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

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

The syntactic analysis of a sentence to establish relationships between head words and words that modify them.

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