Zero-Shot Verification-guided Chain of Thoughts
This addresses the need for automated, scalable verification in reasoning tasks for AI researchers and practitioners, though it is incremental as it builds on existing COT and verification methods.
The paper tackles the problem of verifying reasoning steps in Chain-of-Thought prompts without fine-tuning or manual examples, by introducing zero-shot prompts for decomposition and verification, and shows improved performance on mathematical and commonsense reasoning tasks with various LLMs.
Previous works have demonstrated the effectiveness of Chain-of-Thought (COT) prompts and verifiers in guiding Large Language Models (LLMs) through the space of reasoning. However, most such studies either use a fine-tuned verifier or rely on manually handcrafted few-shot examples. In contrast, in this paper, we focus on LLM-based self-verification of self-generated reasoning steps via COT prompts in a completely zero-shot regime. To explore this setting, we design a new zero-shot prompt, which we call COT STEP, to aid zero-shot decomposition of reasoning steps and design two new zero-shot prompts for LLM-based verifiers. We evaluate the verifiers' ability to classify the correctness of reasoning chains and explore different ways to use verifier scores in guiding reasoning for various mathematical and commonsense reasoning tasks with different LLMs.