Ziqi Jin and Wei Lu. 2025. Self-Harmonized Chain of Thought. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1153–1174, Albuquerque, New Mexico. Association for Computational Linguistics.
Abstract:
Chain-of-thought (CoT) prompting has demonstrated the capacity of large language models
to perform complex reasoning through intermediate steps. While effective, current CoT
methods face challenges: Zero-shot-CoT can
lead to reasoning errors, and Few-shot-CoT requires labor-intensive manual demonstrations.
Auto-CoT attempts to address these issues by
automatically generating diverse demonstrations, but this diversity can lead to inconsistent reasoning patterns. We propose ECHO
(Self-Harmonized Chain of Thought), a novel
method that unifies diverse solution paths into
a consistent and effective reasoning pattern.
ECHO employs an iterative process to refine
and harmonize automatically generated demonstrations, mitigating the limitations of existing
approaches. Our comprehensive experiments
across arithmetic, commonsense, and symbolic
reasoning tasks demonstrate that ECHO outperforms Auto-CoT by an average of 2.8%. These
findings suggest that ECHO represents a significant step towards more robust and generalizable automated reasoning in large language models.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISG2-TC-2023-013
This research / project is supported by the Ministry of Education - Academic Research Fund (AcRF) Tier 2 Programme
Grant Reference no. : MOET2EP20122- 0011
This research / project is supported by the Ministry of Education - Academic Research Fund (AcRF) Tier 3 Programme
Grant Reference no. : MOET32020-0004