CodeIE: Large Code Generation Models are Better Few-Shot Information
Extractors

CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors

9 May 2023

Tianxiang Sun

Xuanjing Huang

Papers citing "CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors"

7 / 7 papers shown

Title
RUIE: Retrieval-based Unified Information Extraction using Large Language Model Xincheng Liao Junwen Duan Yixi Huang Jianxin Wang 31 1 0 18 Sep 2024
Large Language Models Meet NLP: A Survey Libo Qin Qiguang Chen Xiachong Feng Yang Wu Yongheng Zhang Yinghui Li Min Li Wanxiang Che Philip S. Yu ALM LM&MA ELM LRM 38 44 0 21 May 2024
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction Qingyun Wang Zixuan Zhang Hongxiang Li Xuan Liu Jiawei Han Huimin Zhao Heng Ji 32 1 0 18 Jan 2024
Binding Language Models in Symbolic Languages Zhoujun Cheng Tianbao Xie Peng Shi Chengzu Li Rahul Nadkarni ... Dragomir R. Radev Mari Ostendorf Luke Zettlemoyer Noah A. Smith Tao Yu LMTD 109 195 0 06 Oct 2022
Large Language Models are Few-Shot Clinical Information Extractors Monica Agrawal S. Hegselmann Hunter Lang Yoon Kim David Sontag BDL LM&MA 154 327 0 25 May 2022
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq R. Joty S. Hoi 204 1,451 0 02 Sep 2021
A Frustratingly Easy Approach for Entity and Relation Extraction Zexuan Zhong Danqi Chen 146 108 0 24 Oct 2020