Publications

2026

1. NeurIPS 2026 Main Track

   Population-Aligned Persona Generation for LLM-based Social Simulation

   Zhengyu Hu, Jianxun Lian, Zheyuan Xiao, Max Xiong, Yuxuan Lei, and 5 more authors

   Advances in Neural Information Processing Systems 39, 2026. Forthcoming.

   Builds representative persona sets by aligning LLM-generated profiles with real population-level psychometric distributions.

   PDF arXiv BibTeX 38 citations

   @inproceedings{hu2025persona,
     title = {Population-Aligned Persona Generation for LLM-based Social Simulation},
     author = {Hu, Zhengyu and Lian, Jianxun and Xiao, Zheyuan and Xiong, Max and Lei, Yuxuan and Wang, Tianfu and Ding, Kaize and Xiao, Ziang and Yuan, Nicholas Jing and Xie, Xing},
     booktitle = {Advances in Neural Information Processing Systems 39},
     year = {2026},
     note = {Forthcoming.},
   }

2. EMNLP 2026 Main Conference

   When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection

   Zhengyu Hu^*, Zheyuan Xiao^*, Linxin Song, Fengqing Jiang, Yuetai Li, and 8 more authors

   Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, 2026. * Equal contribution.

   Selects verified teacher answers by student-specific learning cost instead of assuming the strongest teacher always teaches best.

   PDF arXiv Proceedings BibTeX 1 citation

   @inproceedings{hu2026studentcentric,
     title = {When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection},
     author = {Hu, Zhengyu and Xiao, Zheyuan and Song, Linxin and Jiang, Fengqing and Li, Yuetai and Xiong, Zhihan and Lin, Junhao and Liu, Yue and Su, Yao and Hu, Lijie and Ding, Kaize and Xiao, Teng and Poovendran, Radha},
     booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
     pages = {31221--31256},
     year = {2026},
     note = {* Equal contribution.},
   }

2025

1. EMNLP 2025 Findings

   Explaining Length Bias in LLM-Based Preference Evaluations

   Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, and 5 more authors

   Findings of the Association for Computational Linguistics: EMNLP 2025, 2025.

   Explains length bias through desirability and information mass, then introduces a length-aligned evaluation protocol.

   PDF arXiv Proceedings BibTeX 79 citations

   @inproceedings{hu2025length,
     title = {Explaining Length Bias in LLM-Based Preference Evaluations},
     author = {Hu, Zhengyu and Song, Linxin and Zhang, Jieyu and Xiao, Zheyuan and Wang, Tianfu and Chen, Zhengyu and Yuan, Nicholas Jing and Lian, Jianxun and Ding, Kaize and Xiong, Hui},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2025},
     pages = {6763--6794},
     year = {2025},
     doi = {10.18653/v1/2025.findings-emnlp.358},
   }

2. NeurIPS 2025 Main Conference

   Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study

   Zhengyu Hu, Jianxun Lian, Zheyuan Xiao, Seraphina Zhang, Tianfu Wang, and 3 more authors

   Advances in Neural Information Processing Systems 38, 2025.

   Introduces a cognitive framework for testing how language models learn from instructors, concepts, and experience.

   PDF arXiv Proceedings OpenReview Poster BibTeX 11 citations

   @inproceedings{hu2025learning,
     title = {Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study},
     author = {Hu, Zhengyu and Lian, Jianxun and Xiao, Zheyuan and Zhang, Seraphina and Wang, Tianfu and Yuan, Nicholas Jing and Xie, Xing and Xiong, Hui},
     booktitle = {Advances in Neural Information Processing Systems 38},
     year = {2025},
   }