Abstract
The chain-of-though (CoT) prompting methods were successful in various natural language processing (NLP) tasks thanks to their ability to unveil the underlying complex reasoning processes. Such reasoning processes typically exhibit highly structured steps. Recent efforts also started investigating methods to encourage more structured reasoning procedures to be captured (cite least to most).In this work, we propose Tab-CoT, a novel tabular-format CoT prompting method, which allows the complex reasoning process to be explicitly modeled in a highly structured manner. Despite its simplicity, we show that our approach is capable of performing reasoning across multiple dimensions (i.e., both rows and columns).We demonstrate our approach’s strong zero-shot and few-shot capabilities through extensive experiments on a range of reasoning tasks.- Anthology ID:
- 2023.findings-acl.651
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10259–10277
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.651
- DOI:
- 10.18653/v1/2023.findings-acl.651
- Cite (ACL):
- Jin Ziqi and Wei Lu. 2023. Tab-CoT: Zero-shot Tabular Chain of Thought. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10259–10277, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Tab-CoT: Zero-shot Tabular Chain of Thought (Ziqi & Lu, Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.651.pdf