@inproceedings{chen-etal-2026-multi-source,
title = "Multi-Source Retrieval and Reasoning for Legal Sentencing Prediction",
author = "Chen, Junjie and
Li, Haitao and
Zhang, Qilei and
Li, Zhenghua and
Zhang, Ya and
Zhou, Quan and
Luo, Cheng and
Liu, Yiqun and
Zhang, Min and
Wu, Yueyue and
Guo, Dongsheng and
Ai, Qingyao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.481/",
pages = "11047--11062",
ISBN = "979-8-89176-459-0",
abstract = "Legal judgment prediction (LJP) aims to predict judicial outcomes from case facts and typically includes law article, charge, and sentencing prediction. While recent methods perform well on the first two subtasks, legal sentencing prediction (LSP) remains difficult due to its need for fine-grained objective knowledge and flexible subjective reasoning. To address these limitations, we propose $MSR^2$, a framework that integrates multi-source retrieval and reasoning in LLMs with reinforcement learning. $MSR^2$ enables LLMs to perform multi-source retrieval based on reasoning needs and applies a process-level reward to guide intermediate subjective reasoning steps. Experiments on two real-world datasets show that $MSR^2$ improves both accuracy and interpretability in LSP, providing a promising step toward practical legal AI. Our code is available at https://github.com/cjj826/MSR2."
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<abstract>Legal judgment prediction (LJP) aims to predict judicial outcomes from case facts and typically includes law article, charge, and sentencing prediction. While recent methods perform well on the first two subtasks, legal sentencing prediction (LSP) remains difficult due to its need for fine-grained objective knowledge and flexible subjective reasoning. To address these limitations, we propose MSR², a framework that integrates multi-source retrieval and reasoning in LLMs with reinforcement learning. MSR² enables LLMs to perform multi-source retrieval based on reasoning needs and applies a process-level reward to guide intermediate subjective reasoning steps. Experiments on two real-world datasets show that MSR² improves both accuracy and interpretability in LSP, providing a promising step toward practical legal AI. Our code is available at https://github.com/cjj826/MSR2.</abstract>
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%0 Conference Proceedings
%T Multi-Source Retrieval and Reasoning for Legal Sentencing Prediction
%A Chen, Junjie
%A Li, Haitao
%A Zhang, Qilei
%A Li, Zhenghua
%A Zhang, Ya
%A Zhou, Quan
%A Luo, Cheng
%A Liu, Yiqun
%A Zhang, Min
%A Wu, Yueyue
%A Guo, Dongsheng
%A Ai, Qingyao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-multi-source
%X Legal judgment prediction (LJP) aims to predict judicial outcomes from case facts and typically includes law article, charge, and sentencing prediction. While recent methods perform well on the first two subtasks, legal sentencing prediction (LSP) remains difficult due to its need for fine-grained objective knowledge and flexible subjective reasoning. To address these limitations, we propose MSR², a framework that integrates multi-source retrieval and reasoning in LLMs with reinforcement learning. MSR² enables LLMs to perform multi-source retrieval based on reasoning needs and applies a process-level reward to guide intermediate subjective reasoning steps. Experiments on two real-world datasets show that MSR² improves both accuracy and interpretability in LSP, providing a promising step toward practical legal AI. Our code is available at https://github.com/cjj826/MSR2.
%U https://aclanthology.org/2026.emnlp-main.481/
%P 11047-11062
Markdown (Informal)
[Multi-Source Retrieval and Reasoning for Legal Sentencing Prediction](https://aclanthology.org/2026.emnlp-main.481/) (Chen et al., EMNLP 2026)
ACL
- Junjie Chen, Haitao Li, Qilei Zhang, Zhenghua Li, Ya Zhang, Quan Zhou, Cheng Luo, Yiqun Liu, Min Zhang, Yueyue Wu, Dongsheng Guo, and Qingyao Ai. 2026. Multi-Source Retrieval and Reasoning for Legal Sentencing Prediction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 11047–11062, Budapest, Hungary. Association for Computational Linguistics.