# ANAH: Analytical Annotation of Hallucinations in Large Language Models

[![arXiv](https://img.shields.io/badge/arXiv-2312.14033-b31b1b.svg)](https://arxiv.org/abs/2405.20315)
[![license](https://img.shields.io/github/license/InternLM/opencompass.svg)](./LICENSE)

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.

More information please refer to our [project page](https://open-compass.github.io/ANAH/).

## 🤗 How to use the model

You have to follow the prompt in [our paper](https://arxiv.org/abs/2405.20315) to annotate the hallucination.

The models follow the conversation format of InternLM2-chat, with the template protocol as:

```python
dict(role='user', begin='<|im_start|>user\n', end='<|im_end|>\n'),
dict(role='assistant', begin='<|im_start|>assistant\n', end='<|im_end|>\n'),
```

## 🖊️ Citation

If you find this project useful in your research, please consider citing:
```
@article{ji2024anah,
  title={ANAH: Analytical Annotation of Hallucinations in Large Language Models},
  author={Ji, Ziwei and Gu, Yuzhe and Zhang, Wenwei and Lyu, Chengqi and Lin, Dahua and Chen, Kai},
  journal={arXiv preprint arXiv:2405.20315},
  year={2024}
}
```