Rejected Assistant Responses From Anthropic HH-RLHF Dialogues

Supervised fine-tuning pairs built from rejected responses in the Anthropic HH-RLHF dataset. Each example provides a multi-turn conversation history formatted with Human/Assistant turns and the subsequent rejected assistant turn.

Use Cases

• Train a dialogue model to recognize and avoid generating rejected assistant responses using the provided conversation history.
• Analyze patterns in rejected assistant turns from the Anthropic HH-RLHF dataset to study undesirable conversational outputs.
• Fine-tune a classifier on multi-turn Human/Assistant dialogue history to predict the likelihood of a response being rejected.

Strengths

• Derived from the established Anthropic HH-RLHF dataset, a known source for human preference data.
• Contains multi-turn conversation history, providing context for each rejected response.
• Tagged with specific categories including NSFW and Toxic, indicating content labeling.

Limitations

• Dataset size, row count, and column structure are unknown, limiting assessment of scale and features.
• Content warnings for NSFW and Toxic material indicate potential bias towards harmful or undesirable examples.
• Lacks sample data and file format details, hindering immediate usability assessment.

Provenance

Source

Anthropic HH-RLHF dataset.

Collection Method

Constructed exclusively from rejected responses in the source dataset, formatted into dialog-style SFT pairs.

Time Range

null

Freshness

Last updated on 2025-08 24.

Geography

Region tag indicates 'us' (United States).