Papers by Kepu Zhang
Legal Mathematical Reasoning with LLMs: Procedural Alignment through Two-Stage Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Existing legal mathematical reasoning models lack structured numerical reasoning . existing models perform poorly on LexNum, while LexPam improves both mathematical accuracy and legal coherence. |
| Approach: | They propose a legal mathematical reasoning benchmark LexNum and LexPam to address this problem . LexPam is a two-stage reinforcement learning framework for efficient legal reasoning training. |
| Outcome: | The proposed framework improves mathematical accuracy and legal coherence . it also improves legal cohesion and generalizes effectively across tasks and domains. |
Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning (2025.findings-emnlp)
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| Challenge: | Current legal large language models lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset. |
| Approach: | They propose a benchmark dataset for Legal Judgment Prediction with Innocent Verdicts that incorporates trichotomous dogmatics into zero-shot prompting and fine-tuning. |
| Outcome: | The proposed dataset extends three widely-used legal datasets through LLM-based augmentation and manual verification. |
Logic Rules as Explanations for Legal Case Retrieval (2024.lrec-main)
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| Challenge: | Recent efforts to learn explainable legal case retrieval models fail to provide faithful and interpretable explanations for legal cases. |
| Approach: | They propose a framework that uses logic rules to explain legal case retrieval results . they extend benchmarks of LeCaRD and ELAM with manually annotated logic rules . |
| Outcome: | The proposed framework is able to provide faithful explanations for legal case retrieval. |
CitaLaw: Enhancing LLM with Citations in Legal Domain (2025.findings-acl)
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| Challenge: | Existing benchmarks have focused on enabling large language models (LLMs) to generate citationsupported outputs. |
| Approach: | They propose to use a citation-based framework to evaluate LLMs' ability to produce legally sound responses with appropriate citations. |
| Outcome: | The proposed framework enables LLMs to retrieve supporting citations from the reference corpus and align these citation with the corresponding sentences in their responses. |
Effective In-Context Example Selection through Data Compression (2024.findings-acl)
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| Challenge: | In-context learning has been validated in large language models, but the mechanism and selection strategy for in-cont example selection lacks systematic and in-depth research. |
| Approach: | They propose a data compression approach to select in-context examples using large language models. |
| Outcome: | The proposed method shows a significant improvement of 5.90% across five real-world datasets using four language models. |