Papers by Zenan Zhai
A Chinese Dataset for Evaluating the Safeguards in Large Language Models (2024.findings-acl)
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Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han, Shom Lin, Zhenxuan Zhang, Angela Zhao, Preslav Nakov, Timothy Baldwin
| Challenge: | a recent study has shown that large language models can produce harmful responses, exposing users to unexpected risks. |
| Approach: | They propose a dataset for the safety evaluation of Chinese LLMs in Mandarin Chinese . they extend the dataset to better identify false negative and false positive examples . |
| Outcome: | The proposed dataset is for the safety evaluation of Chinese LLMs, and is based on a Chinese dataset. |
Loki: An Open-Source Tool for Fact Verification (2025.coling-demos)
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Haonan Li, Xudong Han, Hao Wang, Yuxia Wang, Minghan Wang, Rui Xing, Yilin Geng, Zenan Zhai, Preslav Nakov, Timothy Baldwin
| Challenge: | Loki is an open-source fact-checking tool designed to address the growing problem of misinformation. |
| Approach: | They propose a tool that breaks down the fact-checking task into five steps . they propose LOKI, which offers a semiautomated, human-in-the-loop approach . |
| Outcome: | a new open-source tool is designed to address the growing problem of misinformation . the tool breaks down the fact-checking task into five steps to assist human judgment . |
FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation (2026.findings-eacl)
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Yulia Otmakhova, Thinh Hung Truong, Rahmad Mahendra, Zenan Zhai, Rongxin Zhu, Daniel Beck, Jey Han Lau
| Challenge: | FLUKE introduces controlled variations across linguistic levels and leverages large language models with human validation to generate modifications. |
| Approach: | They propose a framework for assessing model robustness through systematic minimal variations of test data. |
| Outcome: | The proposed framework evaluates models and LLMs across six diverse NLP tasks and shows that they are more robust to natural, fluent modifications than base models. |
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)
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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li
| Challenge: | Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes. |
| Approach: | They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy. |
| Outcome: | The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline. |
A Bag-of-concepts Model Improves Relation Extraction in a Narrow Knowledge Domain with Limited Data (N19-3)
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| Challenge: | Existing methods for relation extraction on small data sets are time-consuming and expensive. |
| Approach: | They propose an automatic relation extraction task with limited annotated data and a narrow knowledge domain. |
| Outcome: | The proposed method outperforms methods of higher complexity on a small clinical corpus. |