  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.

Computation and Language