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)
Research Assistant Professor
I am a Research Assistant Professor at the School of Artificial Intelligence, Shanghai Jiao Tong University, where I work with Prof. Junchi Yan at RethinkLab. I study how to build AI systems that can automate long-horizon, effort-intensive, and creativity-demanding tasks such as research, engineering, and development. My current work focuses on autonomous agents, large language models, and AI4Research. Before joining SJTU, I received my PhD in Data Science from Fudan University, advised by Prof. Xiangdong Zhou, and completed postdoctoral research at Tsinghua University (THUNLP), working with Prof. Zhiyuan Liu and Prof. Maosong Sun. I was also a research intern at the Machine Learning Research Group of Microsoft Research Asia, mentored by Xiao Yang, and at the Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, working with Prof. Yang Yu.