Feb. 22, 2024, 5:43 a.m. | Honghao Gui, Shuofei Qiao, Jintian Zhang, Hongbin Ye, Mengshu Sun, Lei Liang, Huajun Chen, Ningyu Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2305.11527v2 Announce Type: replace-cross
Abstract: Traditional information extraction (IE) methodologies, constrained by pre-defined classes and static training paradigms, often falter in adaptability, especially in the dynamic world. To bridge this gap, we explore an instruction-based IE paradigm in this paper, leveraging the substantial cross-task generalization capabilities of Large Language Models (LLMs). We observe that most existing IE datasets tend to be overly redundant in their label sets, which leads to the inclusion of numerous labels not directly relevant to the …

arxiv bilingual cs.ai cs.cl cs.ir cs.lg dataset extraction information information extraction type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne