Abstract
Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have broadened the scope for improving multi-step reasoning capabilities. We generally divide multi-step reasoning into two phases: *path generation* to generate the reasoning path(s); and *answer calibration* post-processing the reasoning path(s) to obtain a final answer. However, the existing literature lacks systematic analysis on different answer calibration approaches. In this paper, we summarize the taxonomy of recent answer calibration techniques and break them down into step-level and path-level strategies. We then conduct a thorough evaluation on these strategies from a unified view, systematically scrutinizing step-level and path-level answer calibration across multiple paths. Experimental results reveal that integrating the dominance of both strategies tends to derive optimal outcomes. Our study holds the potential to illuminate key insights for optimizing multi-step reasoning with answer calibration.- Anthology ID:
- 2024.nlrse-1.3
- Volume:
- Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Bhavana Dalvi Mishra, Greg Durrett, Peter Jansen, Ben Lipkin, Danilo Neves Ribeiro, Lionel Wong, Xi Ye, Wenting Zhao
- Venues:
- NLRSE | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 25–38
- Language:
- URL:
- https://aclanthology.org/2024.nlrse-1.3
- DOI:
- Cite (ACL):
- Shumin Deng, Ningyu Zhang, Nay Oo, and Bryan Hooi. 2024. Towards A Unified View of Answer Calibration for Multi-Step Reasoning. In Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024), pages 25–38, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Towards A Unified View of Answer Calibration for Multi-Step Reasoning (Deng et al., NLRSE-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.nlrse-1.3.pdf