Papers by Weihang Su
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. |
STARD: A Chinese Statute Retrieval Dataset Derived from Real-life Queries by Non-professionals (2024.findings-emnlp)
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Weihang Su, Yiran Hu, Anzhe Xie, Qingyao Ai, Quezi Bing, Ning Zheng, Yun Liu, Weixing Shen, Yiqun Liu
| Challenge: | Existing statute retrieval benchmarks emphasize formal and professional queries from sources like bar exams and legal case documents . existing retrieval approaches that lack domain-specific knowledge may struggle to capture the meanings of specialized terms accurately. |
| Approach: | They propose a dataset that captures the complexity and diversity of real queries from the general public. |
| Outcome: | The proposed dataset captures the complexity and diversity of real queries from the general public. |
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. |
Augmenting Multi-Agent Communication with State Delta Trajectory (2025.emnlp-main)
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| Challenge: | Multi-agent systems based on large language models (LLMs) have shown to be effective in downstream tasks. |
| Approach: | They propose a protocol that transfers both natural language tokens and token-wise state transition trajectory from one agent to another. |
| Outcome: | The proposed protocol can transfer both natural language tokens and token-wise state transition trajectory from one agent to another. |
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)
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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu
| Challenge: | Existing studies show that advanced LLMs produce text indistinguishable from human writing. |
| Approach: | They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style. |
| Outcome: | The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts. |
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. |
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. |
Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing (2025.findings-acl)
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| Challenge: | Existing knowledge editing approaches directly edit model context without isolating target knowledge from the reasoning path of model inference, resulting in unreliable and low-quality outputs, especially in multi-hop tasks. |
| Approach: | They propose a framework that separates model reasoning from knowledge editing and propose 'DecKER' that allows users to modify specific factual associations without retraining the entire model. |
| Outcome: | The proposed framework significantly improves multi-hop reasoning performance by mitigating knowledge conflicts and preserving reasoning integrity. |
Knowledge Editing through Chain-of-Thought (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods focus on multi-hop QA tasks and require frequent retraining. |
| Approach: | They propose a new knowledge editing framework that updates large language models with new information to maintain their world knowledge without retraining. |
| Outcome: | The proposed method achieves state-of-the-art performance while offering superior generalization, effectiveness, and stability compared to existing methods. |