Glossary

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Recurrent Neural Network(also: RNN)

A recurrent neural network (RNN) is a type of artificial neural network designed to process sequential data by maintaining an internal state (memory) that captures information from previous inputs in the sequence. Unlike feedforward networks, RNNs have connections that loop…

Reinforcement Learning(also: RL)

A type of machine learning where a system learns to make decisions by performing actions in an environment and receiving rewards or penalties based on the outcomes. Unlike supervised learning, which learns from labelled examples, reinforcement learning discovers optimal…

Reinforcement Learning from Human Feedback(also: RLHF)

A machine learning technique used to fine-tune large language models by incorporating human judgments about response quality. Human annotators rank or rate model outputs, and this feedback trains a reward model that guides the LLM toward producing preferred responses. While RLHF…

Relevance Scoring(also: Task Relevance Score, Content Relevance Rating)

The assignment of numerical scores to web page elements indicating how relevant they are to a user's specified task or goal. In systems like Task Mode, relevance scores typically range from 0 (completely irrelevant) to 100 (critical to the task), assigned by large language…

Representational harm(also: Representational bias)

A category of harm caused by AI systems that perpetuate or amplify negative stereotypes, demeaning portrayals, or erasure of particular social groups, distinct from allocative harms that deny resources or opportunities. In disability contexts, representational harms occur when…

Retrieval-Augmented Generation(also: RAG)

An AI technique that enhances the responses of large language models (LLMs) by first retrieving relevant information from an external knowledge base or document collection, then providing that information as context for the model to generate its response. In accessibility…

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