Year: 2026
Type: conference-paper
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-ofthe-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.
Authors: Itay Razumenko, Arnon Sturm, Nir Grinberg
Institution: Ben-Gurion University of the Negev
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W7166823988
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