零样本验证引导的思维链
Zero-Shot Verification-guided Chain of Thoughts
AI总结：
本文提出零样本验证引导的思维链方法，通过新提示COT STEP分解推理步骤，并用两种零样本验证器提示评估推理链正确性，以引导多种LLM完成数学和常识推理任务。
摘要：
先前的研究已证明思维链（COT）提示和验证器在引导大型语言模型（LLMs）进行推理空间探索方面的有效性。然而，大多数此类研究要么使用微调后的验证器，要么依赖手工制作的少样本示例。相比之下，本文聚焦于在完全零样本场景下，通过COT提示对自生成推理步骤进行基于LLM的自验证。为探索这一设置，我们设计了一种新的零样本提示，称为COT STEP，以辅助推理步骤的零样本分解，并设计了两种新的用于基于LLM的验证器的零样本提示。我们评估了验证器对推理链正确性进行分类的能力，并探索了在不同LLM上，针对各种数学和常识推理任务，使用验证器分数引导推理的不同方式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
