Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance

• URL: http://arxiv.org/abs/2504.09586v1
• Date: Sun, 13 Apr 2025 14:12:14 GMT
• Title: Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance
• Authors: Zuoli Tang, Junjie Ou, Kaiqin Hu, Chunwei Wu, Zhaoxin Huan, Chilin Fu, Xiaolu Zhang, Jun Zhou, Chenliang Li,
• Abstract summary: Large language models' (LLMs) reasoning is largely due to the chain-of-thought (CoT) approaches.<n>LLMs are instruction-tuned to provide long and detailed CoT pathways when responding to reasoning-related questions.<n>Human beings are naturally cognitive misers and will prompt language models to give rather short responses.
• Score: 33.16322104912836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have witnessed significant progress in large language models' (LLMs) reasoning, which is largely due to the chain-of-thought (CoT) approaches, allowing models to generate intermediate reasoning steps before reaching the final answer. Building on these advances, state-of-the-art LLMs are instruction-tuned to provide long and detailed CoT pathways when responding to reasoning-related questions. However, human beings are naturally cognitive misers and will prompt language models to give rather short responses, thus raising a significant conflict with CoT reasoning. In this paper, we delve into how LLMs' reasoning performance changes when users provide short-path prompts. The results and analysis reveal that language models can reason effectively and robustly without explicit CoT prompts, while under short-path prompting, LLMs' reasoning ability drops significantly and becomes unstable, even on grade-school problems. To address this issue, we propose two approaches: an instruction-guided approach and a fine-tuning approach, both designed to effectively manage the conflict. Experimental results show that both methods achieve high accuracy, providing insights into the trade-off between instruction adherence and reasoning accuracy in current models.

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