ACL 2023long301 citations
Reasoning with Language Model Prompting: A Survey
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang
Abstract
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions. Resources are available at https://github.com/zjunlp/Prompt4ReasoningPapers (updated periodically).
BibTeX
@inproceedings{qiao-etal-2023-reasoning,
title = "Reasoning with Language Model Prompting: A Survey",
author = "Qiao, Shuofei and
Ou, Yixin and
Zhang, Ningyu and
Chen, Xiang and
Yao, Yunzhi and
Deng, Shumin and
Tan, Chuanqi and
Huang, Fei and
Chen, Huajun",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.294/",
doi = "10.18653/v1/2023.acl-long.294",
pages = "5368--5393"
}