Instruction Tuning

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.

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.

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

A text summarisation approach that generates novel sentences to capture the essential meaning of a document, rather than simply extracting and rearranging existing sentences.

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

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 artificial production of human speech from text, also known as text-to-speech.

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

Fine-Tuning

The process of taking a pretrained model and further training it on a smaller, task-specific dataset.