Multilingual 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.

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 process of producing coherent and contextually relevant text using AI language models.

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

A neural network trained to convert text passages into fixed-dimensional vectors that capture semantic meaning, enabling similarity search, clustering, and retrieval applications.

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

The process of creating a concise and coherent summary of a longer text document while preserving key information.

The process of automatically extracting structured information from unstructured or semi-structured text sources.

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

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

Fine-Tuning

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