Think Beyond Size: Dynamic Prompting for More Effective Reasoning
Keywords: model efficiency, prompt optimization, task-specific prompting, small-scale LLMs, real-time adjustment, efficient problem-solving, adaptive reasoning, incremental prompting, step-wise refinement, model performance improvement, task adaptability, few-shot prompting, scalable models.
TL;DR: Dynamic Prompting enables smaller models to outperform larger ones by adjusting prompts in real-time to solve complex tasks more efficiently.
Abstract: 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.
Primary Area: causal reasoning
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Submission Number: 2648
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