@inproceedings{razumenko-etal-2026-judgemenot,
title = "{J}udge{M}e{N}ot: Personalizing Large Language Models to Emulate Judicial Reasoning in {H}ebrew",
author = "Razumenko, Itay and
Sturm, Arnon and
Grinberg, Nir",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1332/",
doi = "10.18653/v1/2026.findings-acl.1332",
pages = "26735--26753",
ISBN = "979-8-89176-395-1",
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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<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.</abstract>
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%0 Conference Proceedings
%T JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew
%A Razumenko, Itay
%A Sturm, Arnon
%A Grinberg, Nir
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F razumenko-etal-2026-judgemenot
%X 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.
%R 10.18653/v1/2026.findings-acl.1332
%U https://aclanthology.org/2026.findings-acl.1332/
%U https://doi.org/10.18653/v1/2026.findings-acl.1332
%P 26735-26753
Markdown (Informal)
[JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew](https://aclanthology.org/2026.findings-acl.1332/) (Razumenko et al., Findings 2026)
ACL