Welcome to Yifan Wei (Eric)’s Personal Homepage!
I am a Ph.D. student at the Beihang University, School of Computer Science and Engineering. I have spent wonderful time at Institute of Automation, Chinese Academy of Sciences as a master student, advised by Prof Kang Liu.
My research interests lie in the field of natural language processing (NLP), with a focus on Large Language Models (LLMs), including Question Answering and Knowledge Editing. Currently, I am highly intrigued by exploring the intersection of LLMs and AI safety.
To date, I have authored three papers as the first author, which have been published at AI conferences including the CIKM, EMNLP, and AAAI,
with total google scholar citations (
🔥 News
- 2024.09: 🎉🎉 Our paper “Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent” has been accepted to EMNLP 2024!
- 2024.07: 🎉🎉 Our paper “DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism” has been accepted to CIKM 2024!
- 2024.07: 🎉🎉 Our paper “Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models” has been accepted to CIKM 2024 (short paper)!
- 2024.05: 📢 Our paper “EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification” has been accepted to ACL 2024 Findings!
- 2023.10: 🎉 Our paper “MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models” has been accepted to EMNLP 2023 Findings!
- 2023.12: 🎉 Our paper “S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering” has been accepted to ACL 2023 (short paper)!
📝 Publications
Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent
Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu
We present Neeko, an innovative framework designed for efficient multiple characters imitation. Unlike existing methods, Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters.
DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism
Xiaoyan Yu, Yifan Wei, Pu Li, Shuaishuai Zhou, Hao Peng, Li Sun, Liehuang Zhu, Philip S Yu
This paper proposes a personalized federated learning framework with a dual aggregation mechanism for social event detection, namely DAMe.
Yifan Wei, Xiaoyan Yu, Yixuan Weng, Huanhuan Ma, Yuanzhe Zhang, Jun Zhao, Kang Liu
This study investigates the differences between entity and relational knowledge through knowledge editing. Our findings reveal that entity and relational knowledge cannot be directly transferred or mapped to each other.
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification
Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu, Liang Wang
We introduce a large scale Multi-hop fact checking dataset with textual explanations, which can be used to evaluate the explainability of fact verification models.
Yifan Wei, Yisong Su, Huanhuan Ma, Xiaoyan Yu, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu
Findings of the Association for Computational Linguistics: EMNLP 2023
We construct Multiple Sensitive Factors Time QA (MenatQA), which encompasses three temporal factors (scope factor, order factor, counterfactual factor) with total 2,853 samples for evaluating the time comprehension and reasoning abilities of LLMs.
Assessing knowledge editing in language models via relation perspective
Yifan Wei, Xiaoyan Yu, Huanhuan Ma, Fangyu Lei, Yixuan Weng, Ran Song, Kang Liu
S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Questions
Yifan Wei, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu
🚀 Projects
- TableQAKit: A Toolkit for Table-based Question Answering.
📖 Educations
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2021.09 - 2024.07, M.S. in Artificial Intelligence, Institute of Automation, Chinese Academy of Sciences. Advisors: Prof. Kang Liu and Prof. Jun Zhao.
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2024.09 - , Ph.D. candidate at the School of Computer Science and Engineering, Beihang University.
💻 Internships
📅 Academic Services
📖 Reviewers
- Annual Meeting of the Association for Computational Linguistics 2023, 2024, Reviewer
- Annual Conference on Neural Information Processing Systems (NeurIPS) Dataset&Benchmark track 2023, Reviewer
- ACM International Conference on Information and Knowledge Management (CIKM) 2024, PC member