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+ # ANAH: Analytical Annotation of Hallucinations in Large Language Models
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+
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+ [![arXiv](https://img.shields.io/badge/arXiv-2312.14033-b31b1b.svg)](https://arxiv.org/abs/2405.20315)
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+ [![license](https://img.shields.io/github/license/InternLM/opencompass.svg)](./LICENSE)
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+
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+ This page holds the InternLM2-20B model which is trained with the ANAH dataset. It is fine-tuned to annotate the hallucination in LLM's responses.
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+ More information please refer to our [project page](https://open-compass.github.io/ANAH/).
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+
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+ ## 🤗 How to use the model
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+
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+ You have to follow the prompt in [our paper](https://arxiv.org/abs/2405.20315) to annotate the hallucination.
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+ The models follow the conversation format of InternLM2-chat, with the template protocol as:
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+
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+ ```python
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+ dict(role='user', begin='<|im_start|>user\n', end='<|im_end|>\n'),
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+ dict(role='assistant', begin='<|im_start|>assistant\n', end='<|im_end|>\n'),
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+ ```
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+
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+ ## 🖊️ Citation
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+
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+ If you find this project useful in your research, please consider citing:
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+ ```
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+ @article{ji2024anah,
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+ title={ANAH: Analytical Annotation of Hallucinations in Large Language Models},
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+ author={Ji, Ziwei and Gu, Yuzhe and Zhang, Wenwei and Lyu, Chengqi and Lin, Dahua and Chen, Kai},
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+ journal={arXiv preprint arXiv:2405.20315},
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+ year={2024}
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+ }
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+ ```