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8 changes: 4 additions & 4 deletions README_mrag.md
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在公开数据集DoubleBench上，我们对比测评了MDocAgent、Colqwen-gen、ViDoRAG、M3DOCRAG等多模态问答系统。

最终答案的准确性采用LLM作为评判标准进行评估（https://arxiv.org/abs/2306.05685）
最终答案的准确性采用LLM作为评判标准进行评估（[https://arxiv.org/abs/2306.05685](https://arxiv.org/abs/2306.05685)）
。 GPT-4o根据0到10的等级对生成的答案与真实答案的正确性进行评分。得分不低于7分的答案为正确，不高于3分的答案为错误，其余答案为部分正确。

测评结果如下：**JoyAgent的正确率达到76.2%,优于当前其他多模态问答系统。**
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| ViDoRAG | 0.623 | 0.144 | 0.233 |
| M3DOCRAG | 0.538 | 0.138 | 0.324 |

- MDocAgent： 北卡罗来纳大学-2025年（https://arxiv.org/abs/2503.13964）
- MDocAgent： 北卡罗来纳大学-2025年（[https://arxiv.org/abs/2503.13964](https://arxiv.org/abs/2503.13964)）

- ViDoRAG：阿里巴巴NLP实验室-2025年（https://arxiv.org/abs/2502.18017）
- ViDoRAG：阿里巴巴NLP实验室-2025年（[https://arxiv.org/abs/2502.18017](https://arxiv.org/abs/2502.18017)）

- M3DOCRAG：北卡罗来纳大学-2025年（https://arxiv.org/abs/2411.04952）
- M3DOCRAG：北卡罗来纳大学-2025年（[https://arxiv.org/abs/2411.04952](https://arxiv.org/abs/2411.04952)）

- Colqwen-gen：参照组，结果由gpt-4o直接回复生成（不采用RAG）。

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