Awesome-LLM4IE-Papers
Awesome papers about generative Information extraction using LLMs
The organization of papers is discussed in our survey: Large Language Models for Generative Information Extraction: A Survey.
If you find any relevant academic papers that have not been included in our research, please submit a request for an update. We welcome contributions from everyone.
If any suggestions or mistakes, please feel free to let us know via email at derongxu@mail.ustc.edu.cn and chenweicw@mail.ustc.edu.cn. We appreciate your feedback and help in improving our work.
If you find our survey useful for your research, please cite the following paper:
@misc{xu2023large,
title={Large Language Models for Generative Information Extraction: A Survey},
author={Derong Xu and Wei Chen and Wenjun Peng and Chao Zhang and Tong Xu and Xiangyu Zhao and Xian Wu and Yefeng Zheng and Enhong Chen},
year={2023},
eprint={2312.17617},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
📒 Table of Contents
- Information Extraction tasks
- Information Extraction Techniques
- Specific Domain
- Evaluation and Analysis
- Project and Toolkit
- ⭐️ Datasets (with Download Link~)
💡 News
- Update Logs
- The details can be find in
./update_new_papers_list
. - 2024/06/06 Add 41 papers
- 2024/03/30 Add 27 papers
- 2024/03/29 Add 20 papers
- The details can be find in
Information Extraction tasks
A taxonomy by various tasks.
Named Entity Recognition
Models targeting only ner tasks.
Entity Typing
Paper | Venue | Date | Code |
---|---|---|---|
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing | EMNLP Findings | 2023-12 | GitHub |
Generative Entity Typing with Curriculum Learning | EMNLP | 2022-12 | GitHub |