HalluQA

# Evaluating Hallucinations in Chinese Large Language Models

This repository contains data and evaluation scripts of HalluQA (Chinese Hallucination Question-Answering) benchmark.
The full data of HalluQA is in **HalluQA.json**.
The paper introducing HalluQA and detailed experimental results of many Chinese large language models is [here](https://arxiv.org/pdf/2310.03368.pdf).

## Data Collection Pipeline
HalluQA contains 450 meticulously designed adversarial questions, spanning multiple domains, and takes into account Chinese historical culture, customs, and social phenomena. The pipeline of data collection is shown above. At step 1, we write questions which we think may induce model hallucinations. At step 2, we use ChatGPT3.5/Puyu/GLM-130B to generate answers and collect adversarial questions. At step 3, we write multiple correct and wrong answers for each adversarial question and add support evidence. At step 4, we check all annotated question-answer pairs and remove low quality samples.

## Data Examples
We show some data examples of HalluQA here.

## Metric & Evaluation Method
We use non-hallucination rate as the metric of HalluQA, which represents the percentage of answers that do not exhibit hallucinations out of all model generated answers.
For automated evaluation, we use GPT-4 as the evaluator. GPT-4 will judge whether a generated answer exhibit hallucinations based on the given criterias and reference correct answers.
The prompt for GPT-4 based evaluation is in **calculate_metrics.py**

### Run evaluation for your models
1. Install requirements
```
pip install openai
```
2. Run evaluation using our script.
```python
python calculate_metrics.py --response_file_name gpt-4-0613_responses.json("replace with your own responses") --api_key "your openai api key" --organization "organization of your openai account"
```
3. The results and metric will be saved in results.json and non_hallucination_rate.txt respectively.

## Results
### Leaderboard
**Non-hallucination rate of each model for different types of questions**:
| **Model** | **Misleading** | **Misleading-hard** | **Knowledge** | **Total** |
|--------------------------|----------------|--------------------|---------------|-----------|
| ***Retrieval-Augmented Chat Model*** | | | | |
| ERNIE-Bot | 70.86 | 46.38 | 75.73 | 69.33 |
| Baichuan2-53B | 59.43 | 43.48 | 83.98 | 68.22 |
| ChatGLM-Pro | 64.00 | 34.78 | 67.96 | 61.33 |
| SparkDesk | 59.43 | 27.54 | 71.36 | 60.00 |
| ***Chat Model*** | | | | |
| abab5.5-chat | 60.57 | 39.13 | 57.77 | 56.00 |
| gpt-4-0613 | 76.00 | 57.97 | 32.04 | 53.11 |
| Qwen-14B-chat | 75.43 | 23.19 | 30.58 | 46.89 |
| Baichuan2-13B-chat | 61.71 | 24.64 | 32.04 | 42.44 |
| Baichuan2-7B-chat | 54.86 | 28.99 | 32.52 | 40.67 |
| gpt-3.5-turbo-0613 | 66.29 | 30.43 | 19.42 | 39.33 |
| Xverse-13B-chat | 65.14 | 23.19 | 22.33 | 39.11 |
| Xverse-7B-chat | 64.00 | 13.04 | 21.84 | 36.89 |
| ChatGLM2-6B | 55.43 | 23.19 | 21.36 | 34.89 |
| Qwen-7B-chat | 55.43 | 14.49 | 17.48 | 31.78 |
| Baichuan-13B-chat | 49.71 | 8.70 | 23.30 | 31.33 |
| ChatGLM-6b | 52.57 | 20.29 | 15.05 | 30.44 |
| ***Pre-Trained Model*** | | | | |
| Qwen-14B | 54.86 | 23.19 | 24.76 | 36.22 |
| Baichuan2-13B-base | 23.43 | 24.64 | 45.63 | 33.78 |
| Qwen-7B | 48.57 | 20.29 | 16.99 | 29.78 |
| Xverse-13B | 18.86 | 24.64 | 32.52 | 27.33 |
| Baichuan-13B-base | 9.71 | 18.84 | 40.78 | 25.33 |
| Baichuan2-7B-base | 8.00 | 21.74 | 41.26 | 25.33 |
| Baichuan-7B-base | 6.86 | 15.94 | 37.38 | 22.22 |
| Xverse-7B | 12.00 | 13.04 | 29.61 | 20.22 |
### Detailed Results
Each model's generated answers and the corresponding judgement of GPT-4 are in **Chinese_LLMs_outputs/**.

## Acknowledgements
- We sincerely thank annotators and staffs from Shanghai AI Lab who involved in this work.
- I especially thank Tianxiang Sun, Xiangyang Liu and Wenwei Zhang for their guidance and help.
- I am also grateful to Xinyang Pu for her help and patience.

#### Download
:modelscope-code[]{type="git"}

## Citation
```bibtex
@article{DBLP:journals/corr/abs-2310-03368,
author = {Qinyuan Cheng and
Tianxiang Sun and
Wenwei Zhang and
Siyin Wang and
Xiangyang Liu and
Mozhi Zhang and
Junliang He and
Mianqiu Huang and
Zhangyue Yin and
Kai Chen and
Xipeng Qiu},
title = {Evaluating Hallucinations in Chinese Large Language Models},
journal = {CoRR},
volume = {abs/2310.03368},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2310.03368},
doi = {10.48550/arXiv.2310.03368},
eprinttype = {arXiv},
eprint = {2310.03368},
timestamp = {Thu, 19 Oct 2023 13:12:52 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2310-03368.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

# 中文大语言模型（Large Language Model）幻觉评估
本仓库包含中文幻觉问答基准（HalluQA，Chinese Hallucination Question-Answering）数据集与评估脚本。HalluQA的完整数据存储于**HalluQA.json**文件中。介绍HalluQA的论文以及多款中文大语言模型的详细实验结果可参见[此处](https://arxiv.org/pdf/2310.03368.pdf)。

## 数据收集流程
HalluQA包含450道精心设计的对抗性问题，覆盖多个领域，并兼顾了中国历史文化、风俗习惯与社会现象。数据收集流程如上图所示。步骤1：编写我们认为可能诱导模型产生幻觉的问题；步骤2：使用ChatGPT3.5、Puyu、GLM-130B生成答案，并收集对抗性问题；步骤3：为每道对抗性问题编写多个正确与错误答案，并补充支撑证据；步骤4：审核所有标注后的问答对，剔除低质量样本。

## 数据示例
本节展示HalluQA的部分数据样例。

## 评估指标与评估方法
本次评估采用无幻觉率作为HalluQA的评估指标，其定义为模型生成的所有答案中，未出现幻觉的答案占比。
在自动化评估环节，我们采用GPT-4作为评估器。GPT-4将依据给定的评估标准与参考答案，判断生成的答案是否存在幻觉。
基于GPT-4的评估提示词存储于**calculate_metrics.py**文件中。

### 针对你的模型运行评估流程
1. 安装依赖

pip install openai

2. 使用我们的脚本运行评估：
python
python calculate_metrics.py --response_file_name gpt-4-0613_responses.json("replace with your own responses") --api_key "your openai api key" --organization "organization of your openai account"

3. 评估结果与指标将分别保存至results.json与non_hallucination_rate.txt文件中。

## 实验结果
### 排行榜
**各模型针对不同题型的无幻觉率**：
| **模型** | **误导型问题** | **高难度误导型问题** | **知识型问题** | **整体得分** |
|--------------------------|----------------|--------------------|---------------|-----------|
| ***检索增强对话模型*** | | | | |
| ERNIE-Bot | 70.86 | 46.38 | 75.73 | 69.33 |
| Baichuan2-53B | 59.43 | 43.48 | 83.98 | 68.22 |
| ChatGLM-Pro | 64.00 | 34.78 | 67.96 | 61.33 |
| SparkDesk | 59.43 | 27.54 | 71.36 | 60.00 |
| ***纯对话模型*** | | | | |
| abab5.5-chat | 60.57 | 39.13 | 57.77 | 56.00 |
| gpt-4-0613 | 76.00 | 57.97 | 32.04 | 53.11 |
| Qwen-14B-chat | 75.43 | 23.19 | 30.58 | 46.89 |
| Baichuan2-13B-chat | 61.71 | 24.64 | 32.04 | 42.44 |
| Baichuan2-7B-chat | 54.86 | 28.99 | 32.52 | 40.67 |
| gpt-3.5-turbo-0613 | 66.29 | 30.43 | 19.42 | 39.33 |
| Xverse-13B-chat | 65.14 | 23.19 | 22.33 | 39.11 |
| Xverse-7B-chat | 64.00 | 13.04 | 21.84 | 36.89 |
| ChatGLM2-6B | 55.43 | 23.19 | 21.36 | 34.89 |
| Qwen-7B-chat | 55.43 | 14.49 | 17.48 | 31.78 |
| Baichuan-13B-chat | 49.71 | 8.70 | 23.30 | 31.33 |
| ChatGLM-6b | 52.57 | 20.29 | 15.05 | 30.44 |
| ***预训练模型*** | | | | |
| Qwen-14B | 54.86 | 23.19 | 24.76 | 36.22 |
| Baichuan2-13B-base | 23.43 | 24.64 | 45.63 | 33.78 |
| Qwen-7B | 48.57 | 20.29 | 16.99 | 29.78 |
| Xverse-13B | 18.86 | 24.64 | 32.52 | 27.33 |
| Baichuan-13B-base | 9.71 | 18.84 | 40.78 | 25.33 |
| Baichuan2-7B-base | 8.00 | 21.74 | 41.26 | 25.33 |
| Baichuan-7B-base | 6.86 | 15.94 | 37.38 | 22.22 |
| Xverse-7B | 12.00 | 13.04 | 29.61 | 20.22 |
### 详细实验结果
各模型生成的答案以及GPT-4对应的评估判断结果存储于**Chinese_LLMs_outputs/**文件夹中。

## 致谢
- 我们衷心感谢参与本项目的上海人工智能实验室标注人员与工作人员。
- 特别感谢孙天祥、刘向阳与张文伟给予的指导与帮助。
- 同时感谢蒲新阳的帮助与耐心支持。

#### 下载
:modelscope-code[]{type="git"}

## 引用
bibtex
@article{DBLP:journals/corr/abs-2310-03368,
author = {Qinyuan Cheng and
Tianxiang Sun and
Wenwei Zhang and
Siyin Wang and
Xiangyang Liu and
Mozhi Zhang and
Junliang He and
Mianqiu Huang and
Zhangyue Yin and
Kai Chen and
Xipeng Qiu},
title = {Evaluating Hallucinations in Chinese Large Language Models},
journal = {CoRR},
volume = {abs/2310.03368},
year = {2023},
url = {https://doi.org/10.48550/arXiv.2310.03368},
doi = {10.48550/arXiv.2310.03368},
eprinttype = {arXiv},
eprint = {2310.03368},
timestamp = {Thu, 19 Oct 2023 13:12:52 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2310.03368.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}