Reranking

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 Understanding

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

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.

A probabilistic model that assigns probabilities to sequences of words, enabling prediction of the next word in a sequence.

Techniques and architectures that enable language models to process and reason over extremely long input sequences, from tens of thousands to millions of tokens.

An NLP task that identifies and classifies named entities in text into categories like person, organisation, and location.

The technology that converts spoken language into text, also known as automatic speech recognition.

Training a language model to follow natural language instructions by fine-tuning on instruction-response pairs.

The method of dividing long documents into smaller segments for embedding and retrieval, balancing context preservation with optimal chunk sizes for vector search accuracy.

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

A measure of how closely the meanings of two text passages align, computed through embedding comparison and used in duplicate detection, search, and recommendation systems.