JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew

• URL: http://arxiv.org/abs/2604.18041v1
• Date: Mon, 20 Apr 2026 10:05:19 GMT
• Title: JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew
• Abstract summary: We introduce a synthetic-organic supervision pipeline that transforms raw judicial decisions into instruction-tuning data.<n>We compare our approach to state-of-the-art personalization techniques across three different tasks and settings.<n> Notably, our model-generated outputs are indistinguishable from the reasoning of human judges, highlighting the viability of efficient personalization.
• Score: 2.4915542959367403
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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