利用ChatGPT总结争议点以推荐中国相似劳动就业案件的实证评估
An empirical evaluation of using ChatGPT to summarize disputes for recommending similar labor and employment cases in Chinese
AI总结：
本研究提出基于逐项争议点聚类与余弦相似度的劳动案件相似推荐混合机制，验证了用GPT系列生成争议点替代法院整理内容的效果，为大模型在法律案件推荐场景的应用提供参考。
摘要：
我们提出了一种用于推荐相似劳动就业诉讼案件的混合机制。分类器基于法院整理的两个案件的逐项争议点判定相似性。我们对争议点进行聚类，计算争议点之间的余弦相似度，并将结果作为分类任务的特征。实验结果表明，这种混合方法优于我们此前仅考虑争议点聚类信息的系统。我们将法院整理的争议点替换为GPT-3.5和GPT-4生成的逐项争议点，并重复了相同的实验。使用GPT-4生成的争议点可获得更优的结果。尽管使用ChatGPT生成的争议点时，我们的分类器表现稍逊，但结果仍令人满意。因此，我们期望未来的大语言模型能具备实用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
