HH-RLHF: Human Preference Data for Reinforcement Learning from Human Feedback

A dataset likely containing human preference labels for training reinforcement learning from human feedback (RLHF) models. The dataset is published on Kaggle, but specific details about its size, creation date, and authors are not provided in the available metadata. Its title suggests it is related to the 'Helpful and Harmless' (HH) benchmark for aligning language models.

Use Cases

• Training a reward model to predict human preferences for AI-generated text (inferred from domain, verify after download)
• Fine-tuning a language model via Proximal Policy Optimization (PPO) using human feedback data (inferred from domain, verify after download)
• Benchmarking AI alignment techniques on helpfulness and harmlessness criteria (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a major platform for data science and machine learning resources.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Row count, column definitions, and license information are unknown, which limits suitability assessment.
• Data may reflect bias inherent to the specific collection methodology used for the original HH-RLHF benchmark.

Provenance

Source

Kaggle