Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framework for Accurately Inferring case Reversals (FAIR), which models the problem of judgments reversals based on real Chinese judgments. We mine the causes of judgments reversals by causal inference methods and inject the obtained causal relationships into the neural network as a priori knowledge. And then, our framework is validated on a challenging dataset as a legal judgment prediction task. The experimental results show that our framework can tap the most critical factors in judgments reversal, and the obtained causal relationships can effectively improve the neural network's performance. In addition, we discuss the generalization ability of large language models for legal intelligence tasks using ChatGPT as an example. Our experiment has found that the generalization ability of large language models still has defects, and mining causal relationships can effectively improve the accuracy and explain ability of model predictions.

翻译：近年来，人工智能研究人员在法律智能领域取得了重大进展。然而，现有研究尚未关注判决反转中所蕴含的重要价值，这限制了法律智能效率的提升。本文提出一个准确推断案件反转的因果框架（FAIR），该框架基于真实中文判决对判决反转问题进行了建模。我们通过因果推断方法挖掘判决反转的原因，并将获得的因果关联作为先验知识注入神经网络。随后，该框架在法律判决预测任务中，基于具有挑战性的数据集进行了验证。实验结果表明，本框架能够挖掘判决反转中最关键的因素，且所获得的因果关联能有效提升神经网络的性能。此外，我们以ChatGPT为例，讨论了大模型在法律智能任务中的泛化能力。实验发现，大模型的泛化能力仍存在缺陷，而挖掘因果关联可有效提升模型预测的准确性与可解释性。