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arXiv · 2607.05582

Prompting Beats Fine-Tuning: Generative Expected Value Scoring for Statutory Term Retrieval

Alvin Wang·Jaromir Savelka
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Abstract

Legal concepts in statutes are often expressed using vague terms, and practitioners frequently turn to case law to interpret them. We study the task of ranking case-law sentences by their usefulness for explaining a concept or target statutory term, using an established dataset of 26,959 sentences covering 42 U.S. Code concepts labeled into four explanatory-value categories. We compare two families of methods: (i) supervised fine-tuning of encoder-only models (ModernBERT) and (ii) zero-shot prompting of decoder-only models. We show that across all concepts and standard NDCG cutoffs, ModernBERT largely matches earlier BERT-family baselines. In contrast, prompting decoder-only models achieves the strongest overall effectiveness, with our best system surpassing all previously reported state-of-the-art results on this task.

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Alvin Wang, Jaromir Savelka. 2026-07-06. Prompting Beats Fine-Tuning: Generative Expected Value Scoring for Statutory Term Retrieval. https://arxiv.org/abs/2607.05582

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