让大模型学会模仿法官的判案逻辑，尤其适合资源少的场景。
JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew
- 用合成数据+真实判决生成指令数据，实现高效个性化微调
- 在词汇、风格和语义相似度上均显著优于现有方法
- 生成内容与真人法官推理难分真假，适合法律AI落地
尽管大语言模型取得显著进展，但为个体决策者进行个性化仍是个难题。本文提出一种合成-有机监督流程，将原始司法判决转化为指令微调数据，支持在低资源环境下对单个法官进行参数高效微调。我们在三个不同任务和设置下对比了该方法与最先进个性化技术的效果。结果表明，因果语言建模结合合成生成的指令微调显著优于所有基线，在词汇、风格和语义相似度上均有显著提升。尤为关键的是，模型生成的推理内容与人类法官难以区分，证明了即使在低资源条件下，高效个性化也具备可行性。
原文摘要 · Abstract (English)
Despite significant advances in large language models, personalizing them for individual decision-makers remains an open problem. Here, we introduce a synthetic-organic supervision pipeline that transforms raw judicial decisions into instruction-tuning data, enabling parameter-efficient fine-tuning of personalized models for individual judges in low-resource settings. We compare our approach to state-of-the-art personalization techniques across three different tasks and settings. The results show that Causal Language Modeling followed by synthetically generated instruction-tuning significantly outperforms all other baselines, providing significant improvements across lexical, stylistic, and semantic similarity. Notably, our model-generated outputs are indistinguishable from the reasoning of human judges, highlighting the viability of efficient personalization, even in low-resource settings.
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