Summary of An Empirical Evaluation Of Using Chatgpt to Summarize Disputes For Recommending Similar Labor and Employment Cases in Chinese, by Po-hsien Wu et al.
An empirical evaluation of using ChatGPT to summarize disputes for recommending similar labor and employment cases in Chinese
by Po-Hsien Wu, Chao-Lin Liu, Wei-Jie Li
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed hybrid mechanism for recommending similar labor and employment litigation cases utilizes a classifier that determines similarity based on itemized disputes from court-prepared case summaries. The approach involves clustering these disputes, computing cosine similarity between them, and using the results as features for classification tasks. Experimental results demonstrate improved performance compared to a previous system considering only cluster information. To further evaluate this method, the authors replaced court-prepared disputes with those generated by GPT-3.5 and GPT-4, finding that GPT-4-generated disputes led to better results. While a classifier using ChatGPT-generated disputes did not perform as well, satisfactory outcomes were still achieved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to find similar cases in labor and employment lawsuits. They created a system that uses a combination of techniques, including clustering and calculating similarities between case disputes. This approach performed better than an earlier version that only looked at cluster information. To test their method further, they replaced the court-prepared dispute summaries with ones generated by language models GPT-3.5 and GPT-4. They found that using GPT-4-generated disputes led to even better results. Although their system didn’t work as well when using ChatGPT-generated disputes, it still produced good outcomes. |
Keywords
» Artificial intelligence » Classification » Clustering » Cosine similarity » Gpt
