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Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust. (arXiv:2211.03046v1 [cs.CL])
cs.CL updates on arXiv.org arxiv.org
Legal judgment Prediction (LJP), aiming to predict a judgment based on fact
descriptions, serves as legal assistance to mitigate the great work burden of
limited legal practitioners. Most existing methods apply various large-scale
pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent
improvements. However, we discover the fact that the state-of-the-art (SOTA)
model makes judgment predictions according to wrong (or non-casual)
information, which not only weakens the model's generalization capability but
also results in severe social problems like …
arxiv causality judgment knowledge legal power prediction understanding