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.
@article{arxiv.2604.18041,
title = {JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew},
author = {Itay Razumenko and Arnon Sturm and Nir Grinberg},
journal= {arXiv preprint arXiv:2604.18041},
year = {2026}
}