Self-Harmonized Chain of Thought
This work addresses a key bottleneck in automated reasoning for AI researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the problem of inconsistent reasoning patterns in chain-of-thought prompting for large language models by proposing ECHO, a method that harmonizes diverse solution paths, resulting in an average performance improvement of 2.8% over Auto-CoT across various reasoning tasks.
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.