Papers by Zihan Xue
LegalDiscourse: Interpreting When Laws Apply and To Whom (2024.naacl-long)
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| Challenge: | Recent advances in NLP and information retrieval have already enabled practical applications. |
| Approach: | They propose a 'discourse' taxonomy for span-and-relation parsing of legal texts . they use a dataset of 602 state-level law paragraphs with 3,715 discourse spans and 1,671 relations to investigate the increase in liquor licenses and decrease in applicable laws. |
| Outcome: | The proposed model performs poorly at span identification and relation classification, but lags far below human level. |
Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)
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| Challenge: | Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA. |
| Approach: | They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning. |
| Outcome: | The proposed method significantly improves performance in automatic and human evaluation. |
Argument-Aware Approach To Event Linking (2024.findings-acl)
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| Challenge: | Prior research in event linking has mainly borrowed methods from entity linking, overlooking distinct features of events. |
| Approach: | They propose an argument-aware method to improve event linking models by augmenting input text with tagged event argument information. |
| Outcome: | The proposed method improves in-KB and out-of-KB queries and training examples. |
From Script to Stage: Automating Experimental Design for Social Simulations with LLMs (2026.findings-acl)
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| Challenge: | Xu et al., 2024): multi-agent simulations based on large language models are a new paradigm for social science research . traditional experimental design relies on interdisciplinary expertise and technical barriers . Xiaoping and Xin eli argue that LLM-driven agents are unreliable for rigorous experimental design due to hallucinations and limited verifiability. |
| Approach: | They propose a framework for multi-agent experiment design based on script generation . Script Composition, Script Finalization, and Actor Generation are the core phases of the framework . |
| Outcome: | The proposed framework lowers the barrier for social science experimental design and provides scientifically grounded decision support for policy-making. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie
| Challenge: | Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling. |
| Approach: | They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. |
| Outcome: | The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks. |
RoChBert: Towards Robust BERT Fine-tuning for Chinese (2022.findings-emnlp)
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Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang
| Challenge: | Pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts. |
| Approach: | They propose to fuse Chinese phonetic and glyph features into pre-trained models by using a more comprehensive adversarial graph. |
| Outcome: | The proposed framework outperforms existing methods in significant ways on a wide range of tasks while remaining accurate on benign texts. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |