Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning

2026-02-08

AI Summary

This work addresses the instability of reasoning paths in existing chain-of-thought prompting methods, which often stems from insufficient guidance and the limited generalizability of single-strategy approaches across diverse tasks. To overcome these limitations, we propose Diverge-to-Induce Prompting (DIP), a novel framework that first guides large language models to generate multiple high-level reasoning rationales for the same problem, then refines these into detailed step-by-step drafts, and finally integrates them into a unified reasoning plan. DIP introduces, for the first time, a mechanism for generating and fusing multiple reasoning rationales, significantly enhancing the robustness and accuracy of zero-shot reasoning without requiring extensive sampling. Experimental results demonstrate that DIP consistently outperforms current single-strategy prompting methods across multiple benchmark tasks.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

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.

Problem

• Chain-of-Thought prompting
• reasoning instability
• single reasoning strategy
• zero-shot reasoning
• large language models

Innovation

• Diverge-to-Induce Prompting
• multi-rationale induction
• zero-shot reasoning
• Chain-of-Thought prompting
• large language models