opencompass/anah
ANAH: Analytical Annotation of Hallucinations in Large Language Models ✨ Introduction ANAH is a bilingual dataset that offers analytical annotation of hallucinations in LLMs within generative question answering. It can be used to train hallucination detector and perform factulaity alignment, for specific practice refer to ANAH-v2 and Mask-DPO. Each answer sentence in our dataset undergoes rigorous annotation, involving the retrieval of a reference fragment, the… See the full description on the dataset page: https://huggingface.co/datasets/opencompass/anah.
| Author / organization | opencompass |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 204 (as reported by Hugging Face) |
| Likes | 8 |
| Created | 29 May 2024 |
| Last updated at source | 13 March 2025 |
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Connected in the knowledge graph
- vllm-sr/Decision-1.0-Route-0.6BModel · used to train
Recent changes
- 17 Oct 2026 Description: ANAH: Analytical Annotation of Hallucinations in Large Language Models ✨ Introduction ANAH is a bilingual dataset that offers analytical annotation of hallucinations in LLMs within generative question answering. It can be used to train hall → ANAH: Analytical Annotation of Hallucinations in Large Language Models ✨ Introduction ANAH is a bilingual dataset that offers analytical annotation of hallucinations in LLMs within generative question answering. It can be used to train hall
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