Pretrained large language models (LLMs) are increasingly utilized across a wide range of natural language processing (NLP) tasks due to their impressive capabilities as few-shot learners. Recent techniques, such as chain-of-thought (CoT) prompting, have significantly advanced multi-step reasoning by introducing step-by-step decomposition, achieving state-of-the-art results on complex reasoning benchmarks. However, these approaches often rely on static prompting templates that do not adapt to task complexity or errors during the reasoning process. In this work, we introduce Adaptive Prompting, a dynamic and iterative framework designed to enhance reasoning by incorporating real-time adjustments to prompt structures and validation mechanisms.Experimental results demonstrate that Adaptive Prompting significantly improves performance on diverse reasoning benchmarks, including arithmetic reasoning (GSM8K, MultiArith), logical reasoning and commonsense tasks, achieving substantial accuracy gains compared to static prompting baselines. By integrating guided prompts, intermediate validation, and self-corrective steps, our approach enables smaller models to achieve competitive performance with larger counterparts, such as GPT-4, while maintaining computational efficiency. The framework achieves this without requiring fine-tuning or task-specific training data, highlighting the untapped potential of iterative reasoning methods.

翻译：预训练大型语言模型（LLM）因其出色的少样本学习能力，正日益广泛地应用于各类自然语言处理（NLP）任务。近期技术如思维链（CoT）提示通过引入逐步分解机制，显著推进了多步推理能力，在复杂推理基准测试中取得了最先进的成果。然而，这些方法通常依赖静态提示模板，无法根据任务复杂度或推理过程中的错误进行自适应调整。本研究提出自适应提示框架——一种动态迭代的推理增强框架，通过实时调整提示结构与验证机制来优化推理过程。实验结果表明，自适应提示在多种推理基准测试（包括算术推理（GSM8K、MultiArith）、逻辑推理与常识任务）中显著提升性能，相比静态提示基线取得显著准确率增益。通过整合引导提示、中间验证与自校正步骤，本方法使较小模型能在保持计算效率的同时，达到与GPT-4等大型模型相竞争的性能水平。该框架无需微调或任务特定训练数据即可实现上述效果，彰显了迭代式推理方法尚未开发的潜力。