Papers by Huiyuan Xie
Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are used as automatic evaluators to provide accurate and reliable assessments. |
| Approach: | They propose a framework that integrates LLM-based judgment models into a multi-agent system and simulates the interactive client-server polling mechanism. |
| Outcome: | The proposed framework outperforms supervised models trained on annotated judgment data while requiring no human-labeled annotations. |
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling (2021.findings-emnlp)
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| Challenge: | Existing dialog models can generate on-topic utterances but struggle to proactively switch topics. |
| Approach: | They propose a topic-shift aware dialog benchmark based on human topic shift annotations. |
| Outcome: | The proposed benchmark enables chatbots to generate topic-shift responses while still struggling to decide when to change topic. |
LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases (2026.acl-long)
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Yida Cai, Ranjuexiao Hu, Huiyuan Xie, Chenyang Li, Yun Liu, Yuxiao Ye, Zhenghao Liu, Weixing Shen, Zhiyuan Liu
| Challenge: | Legal relations are an important analytical framework for dispute resolution in civil cases. |
| Approach: | They propose a comprehensive schema for legal relations in civil cases with hierarchical taxonomy and definitions of arguments. |
| Outcome: | The proposed schema shows that existing LLMs lack the ability to identify civil legal relations and performance improves on downstream tasks. |
LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. |
| Approach: | They propose a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation that uses a training-free equilibrium approximation algorithm to model dialogue over communicative intents and strategies. |
| Outcome: | The proposed framework improves agents’ communication efficiency by helping them convey their intended meaning more effectively through language. |