偏离以诱导提示：多理性诱导用于零样本推理
Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning
AI总结：
本研究提出DIP框架，通过生成多个多样化的高层理由并诱导最终计划，以提升零样本推理的准确性，克服了传统链式思考提示中推理路径不稳定的问题。
摘要：
为了解决标准链式思考提示中无引导推理路径的不稳定性，最近的方法通过首先引导大型语言模型（LLMs）生成单一推理策略来指导模型。然而，仅依赖一个策略来回答每个问题仍然限制了在多样化任务中的性能。我们提出了偏离以诱导提示（DIP），一个框架，首先提示LLM为每个问题生成多个多样化的高层理由。每个理由随后被扩展成详细的、分步骤的草案计划。最后，这些草案计划被诱导成最终计划。DIP在不依赖资源密集型采样的情况下增强了零样本推理的准确性。实验表明，DIP优于单一策略提示，证明了多计划诱导对基于提示的推理的有效性。
英文摘要：
To address the instability of unguided reasoning paths in standard Chain-of-Thought prompting, recent methods guide large language models (LLMs) by first eliciting a single reasoning strategy. However, relying on just one strategy for each question can still limit performance across diverse tasks. We propose Diverge-to-Induce Prompting (DIP), a framework that first prompts an LLM to generate multiple diverse high-level rationales for each question. Each rationale is then elaborated into a detailed, step-by-step draft plan. Finally, these draft plans are induced into a final plan. DIP enhances zero-shot reasoning accuracy without reliance on resource-intensive sampling. Experiments show that DIP outperforms single-strategy prompting, demonstrating the effectiveness of multi-plan induction for prompt-based reasoning.
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。
