@inproceedings{li-etal-2026-legaldrill,
title = "{L}egal{D}rill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models",
author = "Li, Tianchun and
Liu, Haochen and
Pardeshi, Vishwa and
Wang, Xingchen and
Liu, Tianci and
Zhao, Huijun and
Fan, Wei and
Gao, Jing",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.120/",
pages = "1755--1773",
ISBN = "979-8-89176-394-4",
abstract = "Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost. However, their limited capacity struggles with high-stakes legal reasoning tasks that require coherent statute interpretation and logically consistent deduction. Furthermore, training SLMs for such tasks demands high-quality, concise reasoning trajectories, which are prohibitively expensive to manually collect and difficult to curate via standard rejection sampling, which lacks granularity beyond final verdicts. To address these challenges, we propose LegalDrill, a diagnosis-driven synthesis framework that extracts and iteratively refines reasoning trajectories from a capable teacher via fine-grained prompting, then a self-reflective verification is employed to adaptively select the most effective data for the SLM student. The resulting data empower SLM training through supervised fine-tuning and direct preference optimization. Extensive experiments on several legal benchmarks demonstrate that LegalDrill significantly bolsters the legal reasoning capabilities of representative SLMs while bypassing the need for scarce expert annotations, paving a scalable path toward practical legal reasoning systems."
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<abstract>Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost. However, their limited capacity struggles with high-stakes legal reasoning tasks that require coherent statute interpretation and logically consistent deduction. Furthermore, training SLMs for such tasks demands high-quality, concise reasoning trajectories, which are prohibitively expensive to manually collect and difficult to curate via standard rejection sampling, which lacks granularity beyond final verdicts. To address these challenges, we propose LegalDrill, a diagnosis-driven synthesis framework that extracts and iteratively refines reasoning trajectories from a capable teacher via fine-grained prompting, then a self-reflective verification is employed to adaptively select the most effective data for the SLM student. The resulting data empower SLM training through supervised fine-tuning and direct preference optimization. Extensive experiments on several legal benchmarks demonstrate that LegalDrill significantly bolsters the legal reasoning capabilities of representative SLMs while bypassing the need for scarce expert annotations, paving a scalable path toward practical legal reasoning systems.</abstract>
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%0 Conference Proceedings
%T LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models
%A Li, Tianchun
%A Liu, Haochen
%A Pardeshi, Vishwa
%A Wang, Xingchen
%A Liu, Tianci
%A Zhao, Huijun
%A Fan, Wei
%A Gao, Jing
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F li-etal-2026-legaldrill
%X Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost. However, their limited capacity struggles with high-stakes legal reasoning tasks that require coherent statute interpretation and logically consistent deduction. Furthermore, training SLMs for such tasks demands high-quality, concise reasoning trajectories, which are prohibitively expensive to manually collect and difficult to curate via standard rejection sampling, which lacks granularity beyond final verdicts. To address these challenges, we propose LegalDrill, a diagnosis-driven synthesis framework that extracts and iteratively refines reasoning trajectories from a capable teacher via fine-grained prompting, then a self-reflective verification is employed to adaptively select the most effective data for the SLM student. The resulting data empower SLM training through supervised fine-tuning and direct preference optimization. Extensive experiments on several legal benchmarks demonstrate that LegalDrill significantly bolsters the legal reasoning capabilities of representative SLMs while bypassing the need for scarce expert annotations, paving a scalable path toward practical legal reasoning systems.
%U https://aclanthology.org/2026.acl-industry.120/
%P 1755-1773
Markdown (Informal)
[LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models](https://aclanthology.org/2026.acl-industry.120/) (Li et al., ACL 2026)
ACL
- Tianchun Li, Haochen Liu, Vishwa Pardeshi, Xingchen Wang, Tianci Liu, Huijun Zhao, Wei Fan, and Jing Gao. 2026. LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1755–1773, San Diego, California, USA. Association for Computational Linguistics.