Ruiwen Zhou, Maojia Song, Xiaobao Wu, Sitao Cheng, Xunjian Yin, Yuxi Xie, Zhuoqun Hao, Wenyue Hua, Liangming Pan, Soujanya Poria, Min-Yen Kan
(2026).
Epistemic Context Learning: Building Trust the Right Way in LLM-Based Multi-Agent Systems.
arXiv preprint arXiv:2601.21742.
Jundong Xu, Hao Fei, Yuhui Zhang, Liangming Pan, Qijun Huang, Qian Liu, Preslav Nakov, Min-Yen Kan, William Yang Wang, Mong-Li Lee, Wynne Hsu
(2025).
MuSLR: Multimodal Symbolic Logical Reasoning.
Advances in Neural Information Processing Systems 38 (NeurIPS 2025).
Xuan Long Do, Duy C. Dinh, Hai N. Nguyen, Kenji Kawaguchi, Nancy F. Chen, Shafiq Joty, Min-Yen Kan
(2025).
What Makes a Good Natural Language Prompt?.
In
63nd Annual Meeting of the Association for Computational Linguistics (Volume 1, Long Papers), Vienna, Austria, July 27–August 1st, 2025.
Xuan Long Do, Duong Ngoc Yen, Trong Xuan Do, Anh Tuan Luu, Kenji Kawaguchi, Shafiq Joty, Min-Yen Kan, Nancy F. Chen
(2025).
Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines.
In
Findings of the 63nd Annual Meeting of the Association for Computational Linguistics, Vienna, Austria, July 27–August 1st, 2025.
Xuan Long Do, Hai N. Nguyen, Tiviatis Sim, Hieu Dao, Shafiq Joty, Kenji Kawaguchi, Nancy F. Chen, Min-Yen Kan
(2025).
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs.
In
2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics, Albuquerque, New Mexico April 29–May 4, USA, 2025.
Kokil Jaidka, Tsuhan Chen, Simon Chesterman, Wynne Hsu, Min-Yen Kan, Mohan Kankanhalli, Mong-Li Lee, Gyula Seres, Terence Sim, Araz Taeihagh, Anthony Tung, Xiaokui Xiao, Audrey Yue
(2025).
Misinformation, Disinformation, and Generative AI: Implications for Perception and Policy.
Digit. Gov.: Res. Pract..
Minzhi Li (Ella), Zhengyuan Liu, Shumin Deng, Shafiq Joty, Nancy Chen, Min-Yen Kan
(2025).
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation.
Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 2025.
Xuan Long Do, Duong Ngoc Yen, Anh Tuan Luu, Kenji Kawaguchi, Min-Yen Kan, Nancy F. Chen
(2024).
Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models.
In
2024 Conference on Empirical Methods in Natural Language Processing, November 12 –16 Miami, Florida, USA, 2024.
Xuan Long Do, Yiran Zhao, Hannah Brown, Yuxi Xie, James Xu Zhao, Nancy F. Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
(2024).
Prompt Optimization via Adversarial In-Context Learning.
In
62nd Annual Meeting of the Association for Computational Linguistics (Volume 1, Long Papers), Bangkok, Thailand August 11–16, 2024.