ORGANISATION

Anthropic HH-RLHF

Human preference dataset for helpfulness and harmlessness, used to train and evaluate RLHF and alignment methods.

About Dataset

The Anthropic HH-RLHF dataset is a collection of human preference comparisons between pairs of model responses, labeled according to which response is more helpful or more harmless. Released by Anthropic, the dataset was collected through structured conversations where annotators compared and ranked alternative assistant responses across a range of prompts, including some adversarial or red-teaming style interactions. It is designed to capture nuanced human judgments about response quality and safety.

Purpose of Dataset

Hh-rlhf Is Primarily Used For Reinforcement Learning From Human Feedback (Rlhf) And Related Alignment Techniques, Training Reward Models And Fine-tuning Language Models To Be More Helpful And Less Harmful. Researchers Use It To Study Human Preference Modeling, Safety Alignment, And The Trade-offs Between Helpfulness And Harmlessness In Conversational Ai Systems. It Is A Widely Referenced Dataset In Ai Safety And Alignment Research.