Machine learning shows promise in predicting the outcome of legal cases, but most research has concentrated on civil law cases rather than case law systems. We identified two unique challenges in making legal case outcome predictions with case law. First, it is crucial to identify relevant precedent cases that serve as fundamental evidence for judges during decision-making. Second, it is necessary to consider the evolution of legal principles over time, as early cases may adhere to different legal contexts. In this paper, we proposed a new model named PILOT (PredictIng Legal case OuTcome) for case outcome prediction. It comprises two modules for relevant case retrieval and temporal pattern handling, respectively. To benchmark the performance of existing legal case outcome prediction models, we curated a dataset from a large-scale case law database. We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.

我们提出了一种名为PILOT（PredictIng Legal case OuTcome）的新模型，用于案例结果预测。该模型由相关案例检索和时间模式处理两个模块组成，通过对大规模案例数据库进行实验，我们展示了准确识别先例案例和缓解时间转移对案例法预测的重要性，并显示出显著改进，超过之前专注于民事案件结果预测的方法。

PILOT：案例法律结果预测