Papers by Qingyao Wu
SimVBG: Simulating Individual Values by Backstory Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have strong human-like capabilities, but rarely simulating individualized human values. |
| Approach: | They propose a framework that simulates individual values based on individual backstories . they use structured data on an individual to transform their backstoried information to a backstory . |
| Outcome: | The proposed framework improves top-1 accuracy by more than 10% over retrieval-augmented generation methods. |
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. |
DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models (2024.acl-long)
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| Challenge: | Existing dynamic RAG methods fail to address the information needs of large language models (LLMs) despite their impressive capabilities, these models often produce text that seems coherent and plausible but factually incorrect, a problem commonly known as hallucination. |
| Approach: | They propose a dynamic retrieval augmented generation paradigm that actively decides when and what to retrieve during the text generation process of Large Language Models. |
| Outcome: | The proposed framework achieves superior performance over 4 knowledge-intensive generation datasets. |
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies on hallucination detection for LLMs focus on how to identify possible factrelated errors in outputs. |
| Approach: | They propose an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods in hallucination detection. |
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. |
Digging out Discrimination Information from Generated Samples for Robust Visual Question Answering (2023.findings-acl)
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| Challenge: | Existing methods to solve this problem rely on additional annotations and generate negative samples . |
| Approach: | They propose a method to Dig out Discrimination information from Generated samples to address these limitations. |
| Outcome: | The proposed method improves on the visual question-answering datasets. |
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. |