Papers by Senbo Zhang

2 papers
Less is More: Knowledge-Aware Compression for Long Legal Judgment Prediction (2026.findings-acl)

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Challenge: Recent advances leverage large language models (LLMs) for legal reasoning, but they face high computational costs and information degradation when handling long cases.
Approach: They propose a framework that selectively retains legally relevant information while reducing redundant or less informative content, enabling efficient and accurate long-context reasoning.
Outcome: The proposed framework outperforms existing methods on four real-world datasets spanning multiple jurisdictions and languages.
DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)

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Challenge: Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning.
Approach: They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims.
Outcome: The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches.

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