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

ACL ARR 2025 February Submission6756 Authors

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.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: prompting, robustness, fine-tuning

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 6756