Think Beyond Size: Adaptive Prompting for More Effective Reasoning
Kamesh R
TL;DR
The paper addresses the limitations of static prompting for complex reasoning in large language models and introduces Adaptive Prompting, a dynamic, iterative framework with guided reasoning, intermediate validation, and self-correction. It presents a multi-stage workflow that decomposes problems, validates intermediate steps, and refines solutions to reduce error propagation without requiring fine-tuning. Empirical results on arithmetic and commonsense benchmarks show that smaller models like Gemma 9B can achieve performance competitive with larger models such as GPT-4, using adaptive prompting to close the gap on several tasks. The approach promises improved efficiency and robustness, offering a practical route to scalable, reasoning-enabled AI systems.
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.