Papers by Yueyue Wu
CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding (2023.emnlp-main)
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| Challenge: | Existing legal-oriented PLMs rely on replacing general domain training data with legal data or extending the input length to fit the long-length characteristic of legal data. |
| Approach: | They propose a legal document encoder that leverages fine-grained legal knowledge in both the data sampling and pre-training phases. |
| Outcome: | The proposed model outperforms existing general domain pre-training models and legal-specific pre-trainers on multiple benchmarks. |
LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)
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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang
| Challenge: | Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making. |
| Approach: | They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain. |
| Outcome: | The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. |
Chinese Court Simulation with LLM-Based Agents System (2026.findings-acl)
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Kaiyuan Zhang, Jiaqi Li, Yueyue Wu, Haitao Li, Cheng Luo, Shaokun Zou, Yujia Zhou, Weihang Su, Yiqun Liu, Qingyao Ai
| Challenge: | Existing studies have neglected the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulators in practice. |
| Approach: | They propose a court simulation paradigm based on the real-world procedure structure of Chinese courts and a framework that focuses on both legal judgment prediction and court procedure analysis. |
| Outcome: | The proposed model outperforms judges and lawyers from the real trials in many aspects. |