Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918512515088384 |
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| author | Chen, Po-Chun Huang, Hen-Hsen Chen, Hsin-Hsi |
| author_facet | Chen, Po-Chun Huang, Hen-Hsen Chen, Hsin-Hsi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08028 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning Chen, Po-Chun Huang, Hen-Hsen Chen, Hsin-Hsi Computation and Language Artificial Intelligence 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. |
| title | Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2602.08028 |