  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.
