Papers by Zhengxiang Wang
Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors (2025.findings-emnlp)
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| Challenge: | Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation. |
| Approach: | They evaluate LLMs' ability to imitate personal writing styles via in-context learning from user-authored samples. |
| Outcome: | The proposed model can imitate personal writing styles from a small number of user-authored samples. |
LVLMs and Humans Ground Differently in Referential Communication (2026.acl-long)
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Peter Zeng, Weiling Li, Amie J. Paige, Zhengxiang Wang, Panagiotis Kaliosis, Dimitris Samaras, Gregory J. Zelinsky, Susan Brennan, Owen Rambow
| Challenge: | generative AI agents cannot model common ground in a way that enables smooth communication . a recent study examined whether large language models and large vision language models engage in grounding as human discourse partners do . |
| Approach: | They propose to use referential communication to model common ground between a pair of directors and a picture matching system. |
| Outcome: | The proposed experiment shows that generative AI agents cannot model common ground . human conversation relies on common ground accrued and updated by interacting partners . |
LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing (2025.acl-long)
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| Challenge: | a growing number of studies have indicated the general usefulness of LLMs for automated writing assessments. |
| Approach: | They propose a framework that evaluates LLMs' ability to provide scores and comments based on multiple assessment criteria. |
| Outcome: | The proposed framework is interpretable, cost-efficient, scalable, and reproducible . it is compared to existing methods that rely on manual judgments . |
LVLMs are Bad at Overhearing Human Referential Communication (2025.emnlp-main)
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| Challenge: | a crucial skill for embodied AI agents working with humans is grounding in referential communication. |
| Approach: | They use large vision language models to overhear spontaneous conversations between humans . they find that current LVLMs fail to show consistent performance improvement . |
| Outcome: | The proposed models fail to show consistent performance improvement over previous models . the authors release the results to facilitate future research . |