Legal judgment prediction is essential for enhancing judicial efficiency. In
this work, we identify that existing large language models (LLMs) underperform
in this domain due to challenges in understanding case complexities and
distinguishing between similar charges. To adapt LLMs for effective legal
judgment prediction, we introduce the Ask-Discriminate-Predict (ADAPT)
reasoning framework inspired by human judicial reasoning. ADAPT involves
decomposing case facts, discriminating among potential charges, and predicting
the final judgment. We further enhance LLMs through fine-tuning with multi-task
synthetic trajectories to improve legal judgment prediction accuracy and
efficiency under our ADAPT framework. Extensive experiments conducted on two
widely-used datasets demonstrate the superior performance of our framework in
legal judgment prediction, particularly when dealing with complex and confusing
charges.

通过引入基于人类司法推理的问 - 辨 - 判 (ADAPT) 推理框架，将大型语言模型 (LLMs) 用于有效的法律判决预测，并通过多任务合成轨迹的微调来提高准确性和效率，本论文在两个广泛使用的数据集上进行了详尽的实验证明了我们的框架在法律判决预测方面的优越性，尤其是对于复杂和混乱的指控。