Papers by Zhenzhen Li
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. |
MCP-Guard: A Multi-Stage Defense-in-Depth Framework for Securing Model Context Protocol in Agentic AI (2026.findings-acl)
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Wenpeng Xing, Zhonghao Qi, Yupeng Qin, Yilin Li, Caini Chang, Jiahui Yu, Changting Lin, Zhenzhen Xie, Meng Han
| Challenge: | Large Language Models (LLMs) are vulnerable to jailbreak, authors say . authors propose a robust, layered defense architecture designed for LLM–tool interactions . |
| Approach: | They propose a robust, layered defense architecture designed for LLM–tool interactions . they propose XCP-Guard, which employs a three-stage detection pipeline . |
| Outcome: | The proposed model achieves 96.01% accuracy in identifying adversarial prompts . the model is based on a three-stage detection pipeline that balances efficiency with accuracy . |
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text (2022.findings-naacl)
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| Challenge: | Existing approaches to few-shot relation classification have limited labeled examples . a prototype encoder from definition and an instance is needed to learn relation instance classification . |
| Approach: | They propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. |
| Outcome: | The proposed encoder outperforms state-of-the-art methods on several datasets. |