mqqw1/DISC-Law-SFT

--- language: - zh tags: - legal size_categories: - 100M<n<1B license: apache-2.0 --- # DISC-Law-SFT Dataset Legal Intelligent systems in Chinese require a combination of various abilities, including legal text understanding and generation. To achieve this, we have constructed a high-quality supervised fine-tuning dataset called DISC-Law-SFT, which covers different legal scenarios such as legal information extraction, legal judgment prediction, legal document summarization, and legal question answering. DISC-Law-SFT comprises two subsets, DISC-Law-SFT-Pair and DISC-Law-SFT-Triplet. The former aims to introduce legal reasoning abilities to the LLM, while the latter helps enhance the model's capability to utilize external legal knowledge. For more detailed information, please refer to our [technical report](https://arxiv.org/abs/2309.11325) or [paper](https://link.springer.com/chapter/10.1007/978-981-97-5569-1_19). The distribution of the dataset is: <img src="" alt="" width=""/> <table> <tr> <th>Dataset</th> <th>Task/Source</th> <th>Size</th> <th>Scenario</th> </tr> <tr> <td rowspan="10">DISC-Law-SFT-Pair</td> <td>Legal information extraction</td> <td>32K</td> <td rowspan="7">Legal professional assistant</td> </tr> <tr> <td>Legal event detection</td> <td>27K</td> </tr> <tr> <td>Legal case classification</td> <td>20K</td> </tr> <tr> <td>Legal judgement prediction</td> <td>11K</td> </tr> <tr> <td>Legal case matching</td> <td>8K</td> </tr> <tr> <td>Legal text summarization</td> <td>9K</td> </tr> <tr> <td>Judicial public opinion summarization</td> <td>6K</td> </tr> <tr> <td>Legal question answering</td> <td>93K</td> <td>Legal consultation services</td> </tr> <tr> <td>Legal reading comprehension</td> <td>38K</td> <td rowspan="2">Judicial examination assistant</td> </tr> <tr> <td>Judicial examination</td> <td>12K</td> </tr> <tr> <td rowspan="2">DISC-Law-SFT-Triple</td> <td>Legal judgement prediction</td> <td>16K</td> <td>Legal professional assistant</td> </tr> <tr> <td>Legal question answering</td> <td>23K</td> <td>Legal consultation services</td> </tr> <tr> <td rowspan="2">General</td> <td>Alpaca-GPT4</td> <td>48K</td> <td rowspan="2">General scenarios</td> </tr> <tr> <td>Firefly</td> <td>60K</td> </tr> <tr> <td>Total</td> <td colspan="3">403K</td> </tr> </table> We currently open-source most of the DISC-Law-SFT Dataset. More detail and news check our [homepage](https://github.com/FudanDISC/DISC-LawLLM) ! ## Citation If our project has been helpful for your research and work, please kindly cite our work as follows: ``` @misc{yue2023disclawllm, title={DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services}, author={Shengbin Yue and Wei Chen and Siyuan Wang and Bingxuan Li and Chenchen Shen and Shujun Liu and Yuxuan Zhou and Yao Xiao and Song Yun and Xuanjing Huang and Zhongyu Wei}, year={2023}, eprint={2309.11325}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{yue2024lawllm, title={LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval}, author={Yue, Shengbin and Liu, Shujun and Zhou, Yuxuan and Shen, Chenchen and Wang, Siyuan and Xiao, Yao and Li, Bingxuan and Song, Yun and Shen, Xiaoyu and Chen, Wei and others}, booktitle={International Conference on Database Systems for Advanced Applications}, pages={304--321}, year={2024}, organization={Springer} } ```--- 语言： - 中文 标签： - 法律 规模类别： - 100M < n < 1B 许可证：Apache-2.0 --- # DISC-Law-SFT 数据集 中文法律智能系统需融合多项核心能力，涵盖法律文本理解与生成。为此，我们构建了高质量的监督微调（supervised fine-tuning, SFT）数据集DISC-Law-SFT，其覆盖法律信息抽取、法律判决预测、法律文档摘要、法律问答等多样法律场景。DISC-Law-SFT包含两个子集：DISC-Law-SFT-Pair与DISC-Law-SFT-Triplet。前者旨在为大语言模型（Large Language Model）引入法律推理能力，后者则可助力提升模型利用外部法律知识的能力。如需获取更详细的信息，请参阅我们的[技术报告](https://arxiv.org/abs/2309.11325)与[论文](https://link.springer.com/chapter/10.1007/978-981-97-5569-1_19)。数据集的分布情况如下： <img src="" alt="" width=""/> <table> <tr> <th>数据集</th> <th>任务/来源</th> <th>规模</th> <th>应用场景</th> </tr> <tr> <td rowspan="10">DISC-Law-SFT-Pair</td> <td>法律信息抽取</td> <td>32K</td> <td rowspan="7">法律专业助理</td> </tr> <tr> <td>法律事件检测</td> <td>27K</td> </tr> <tr> <td>法律案件分类</td> <td>20K</td> </tr> <tr> <td>法律判决预测</td> <td>11K</td> </tr> <tr> <td>法律案例匹配</td> <td>8K</td> </tr> <tr> <td>法律文本摘要</td> <td>9K</td> </tr> <tr> <td>司法舆情摘要</td> <td>6K</td> </tr> <tr> <td>法律问答</td> <td>93K</td> <td>法律咨询服务</td> </tr> <tr> <td>法律阅读理解</td> <td>38K</td> <td rowspan="2">司法考试助理</td> </tr> <tr> <td>司法考试</td> <td>12K</td> </tr> <tr> <td rowspan="2">DISC-Law-SFT-Triple</td> <td>法律判决预测</td> <td>16K</td> <td>法律专业助理</td> </tr> <tr> <td>法律问答</td> <td>23K</td> <td>法律咨询服务</td> </tr> <tr> <td rowspan="2">通用</td> <td>Alpaca-GPT4</td> <td>48K</td> <td rowspan="2">通用场景</td> </tr> <tr> <td>Firefly</td> <td>60K</td> </tr> <tr> <td>总计</td> <td colspan="3">403K</td> </tr> </table> 我们目前开源了DISC-Law-SFT数据集的大部分内容。更多详情与最新动态，请访问我们的[主页](https://github.com/FudanDISC/DISC-LawLLM)！ ## 引用 若本项目对你的研究与工作有所帮助，请按以下方式引用我们的成果： @misc{yue2023disclawllm, title={DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services}, author={Shengbin Yue and Wei Chen and Siyuan Wang and Bingxuan Li and Chenchen Shen and Shujun Liu and Yuxuan Zhou and Yao Xiao and Song Yun and Xuanjing Huang and Zhongyu Wei}, year={2023}, eprint={2309.11325}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{yue2024lawllm, title={LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval}, author={Yue, Shengbin and Liu, Shujun and Zhou, Yuxuan and Shen, Chenchen and Wang, Siyuan and Xiao, Yao and Li, Bingxuan and Song, Yun and Shen, Xiaoyu and Chen, Wei and others}, booktitle={International Conference on Database Systems for Advanced Applications}, pages={304--321}, year={2024}, organization={Springer} }