OODREB: Benchmarking State-of-the-Art Methods for Out-Of-Distribution Generalization on Relation Extraction
摘要
We serve as the first effort to study out-of-distribution (OOD) problems in RE by constructing OODREB and investigating SOTA RE methods in both i.i.d. and OOD settings. Our benchmark reveals that existing methods struggle, fail to learn causality, and are far from deployment in real-world applications.
类型
出版物
In Proceedings of the ACM Web Conference (WWW 2024)
Authors
Haotian Chen
(he/him)
Assistant Researcher
Haotian Chen is an Assistant Researcher at the School of Artificial Intelligence, Shanghai Jiao Tong University, working with Prof. Junchi Yan at RethinkLab. His research goal is to understand and develop AI for automating tasks that require extensive time, effort, and creative thinking. He works on automating data-driven scientific research, contributing to both alleviating the burden on humans and revolutionizing human productivity. His research focuses on Autonomous Agents, Large Language Models, and AI4Research. He received his PhD in Data Science from Fudan University and completed postdoctoral research at Tsinghua University (THUNLP), where he worked with Prof. Zhiyuan Liu and Prof. Maosong Sun. He was also a research intern at Microsoft Research Asia, where the RD-Agent project he co-developed was featured in the Microsoft Build 2025 Global Keynote.