Natural Language Understanding

Natural Language Processing

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

Seq2Seq Model

A neural network architecture that maps an input sequence to an output sequence, used in translation and summarisation.

Latent Dirichlet Allocation

A generative probabilistic model for discovering topics in a collection of documents.

Text Embedding

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

Semantic Search

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

Vector Database

A database optimised for storing and querying high-dimensional vector embeddings for similarity search.

Constitutional AI

An approach to AI alignment where models are trained to follow a set of principles or constitution.

Natural Language Generation

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

Document Understanding

AI systems that extract structured information from unstructured documents by combining optical character recognition, layout analysis, and natural language comprehension.

Slot Filling

The task of extracting specific parameter values from user utterances to fulfil a detected intent, such as identifying dates, locations, and names in booking requests.

Cross-Lingual Transfer

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

Text Embedding Model

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

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

Techniques for identifying when AI language models generate plausible but factually incorrect or unsupported content.

A fine-grained sentiment analysis approach that identifies opinions directed at specific aspects or features of an entity, such as a product's price, quality, or design.

A two-stage retrieval process where an initial set of candidate documents is rescored by a more powerful model to improve the relevance ordering of search results.

Connecting language model outputs to real-world knowledge, facts, or data sources to improve factual accuracy.

A language model trained on text from dozens or hundreds of languages simultaneously, enabling cross-lingual understanding and generation without language-specific fine-tuning.

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

A neural network trained on massive text corpora that can generate, understand, and reason about natural language.