Summary of Legal Fact Prediction: the Missing Piece in Legal Judgment Prediction, by Junkai Liu et al.
Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction
by Junkai Liu, Yujie Tong, Hui Huang, Bowen Zheng, Yiran Hu, Peicheng Wu, Chuan Xiao, Makoto Onizuka, Muyun Yang, Shuyuan Zheng
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 This AI research paper presents a novel approach to legal judgment prediction (LJP) by introducing the task of legal fact prediction (LFP). The existing LJP models rely on established legal facts, which are challenging to obtain in the early stages of litigation. To address this limitation, the authors propose an LFP model that takes evidence submitted by litigants and predicts legal facts. This enables fact-based LJP technologies to perform predictions even without ground-truth legal facts. The paper also introduces the first benchmark dataset for evaluating the LFP task, called LFPBench. Experimental results on LFPBench demonstrate the effectiveness of LFP-empowered LJP and identify promising research directions for LFP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Legal judgment prediction is a crucial tool in litigation that helps litigants and their lawyers forecast outcomes and refine strategies. Currently, existing models rely on established legal facts, which can be hard to obtain early on. The authors propose a new task called legal fact prediction, where they use evidence submitted by litigants to predict these legal facts. This innovation allows LJP technologies to work even without knowing the outcome. The researchers also created a benchmark dataset for evaluating this task and showed that it works well in their experiments. |
