AppellateGen: A Benchmark for Appellate Legal Judgment Generation
Existing research on legal judgment generation has predominantly focused on first-instance trials, overlooking the complex dialectical reasoning required in appellate proceedings that involve evaluating initial judgments alongside new evidence. This work addresses this gap by introducing AppellateGen, the first benchmark dataset for appellate judgment generation, comprising 7,351 paired case records. We further propose a Standard Operating Procedure (SOP)-driven Legal Multi-Agent System (SLMAS) that decomposes judgment generation into distinct stages—issue identification, evidence retrieval, and opinion drafting—explicitly modeling the causal dependencies among judicial phases. Experimental results demonstrate that SLMAS significantly enhances the logical consistency of generated judgments, though large language models still face notable challenges in handling the intricate reasoning demands of appellate adjudication. The dataset and code are publicly released.