用GPT生成纠纷摘要，提升劳动案件推荐准确率
An empirical evaluation of using ChatGPT to summarize disputes for recommending similar labor and employment cases in Chinese
- 融合聚类与余弦相似度，以纠纷条目为特征进行案件推荐
- GPT-4生成的纠纷摘要使推荐效果优于旧系统
- 验证大模型生成内容在法律实务中的实用潜力
我们提出一种混合机制，用于推荐劳动就业诉讼中的相似案例。该分类器基于两案中法院整理的争议事项判断相似性，通过聚类争议事项、计算事项间余弦相似度，并将结果作为分类特征。实验表明，该混合方法优于仅使用争议聚类信息的旧系统。我们将法院提供的争议事项替换为GPT-3.5和GPT-4生成的条目，并重复实验。使用GPT-4生成的争议事项取得更好效果。尽管使用ChatGPT生成的争议事项时分类器表现未达最优，但结果仍令人满意。因此，我们期望未来的大语言模型能真正具备实际应用价值。
原文摘要 · Abstract (English)
We present a hybrid mechanism for recommending similar cases of labor and employment litigations. The classifier determines the similarity based on the itemized disputes of the two cases, that the courts prepared. We cluster the disputes, compute the cosine similarity between the disputes, and use the results as the features for the classification tasks. Experimental results indicate that this hybrid approach outperformed our previous system, which considered only the information about the clusters of the disputes. We replaced the disputes that were prepared by the courts with the itemized disputes that were generated by GPT-3.5 and GPT-4, and repeated the same experiments. Using the disputes generated by GPT-4 led to better results. Although our classifier did not perform as well when using the disputes that the ChatGPT generated, the results were satisfactory. Hence, we hope that the future large-language models will become practically useful.
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