小模型通过通用策略和迭代纠错，低成本实现大模型的推理能力
Bridging the Reasoning Gap: Small LLMs Can Plan with Generalised Strategies
- 用大模型生成通用解题策略，指导小模型
- 通过反复提示修正错误，提升小模型表现
- 成本降低30%仍达大模型水平，适合资源有限场景
近期大型语言模型（LLMs）在推理能力上的进展表明，其解决简单规划任务的能力显著提升。然而，由于推理能力的增强依赖于模型规模与复杂度，运行这些模型带来的财务与计算成本也随之上升。这一趋势引发对可及性的担忧，以及未来性能提升是否能持续跟上模型膨胀速度的疑问。本文提出两种方法，以增强资源消耗较少的轻量级LLM的推理能力：(1) 由更耗资源的LLM生成特定领域内通用的解题策略，供小模型使用；(2) 利用小模型的成本优势，通过迭代提示方式使其逐步修正自身方案中的错误。在规划与数学推理任务上的实证结果表明，这些方法使小模型性能达到与大模型相当的水平，且成本仅为后者的极小部分。此外，实验显示使用通用策略平均使小模型成本降低约30%。
原文摘要 · Abstract (English)
Recent advancements in the reasoning skills of Large Language Models (LLMs) demonstrate an increase in the ability of LLMs to solve simple planning tasks. However, as long as the driving force behind improved reasoning capability is the size and complexity of the model, the financial and computational costs associated with running them will also increase. This trend raises questions about continued accessibility and whether these improvements will increase at the same pace as models continue to grow in size and expense. We propose two approaches to enhance the reasoning ability of less resource-intensive LLMs. (1) Provide them with a generalised strategy for solving tasks within a given domain, generated by a more resource-intensive LLM. (2) Exploit their cost-effectiveness by iteratively prompting these models to correct errors in their proposed solutions. Our empirical results from planning and mathematical reasoning tasks demonstrate that these methods improve the performance of less resource-intensive LLMs to levels comparable with their more resource-intensive counterparts, at a fraction of the cost. Additionally, we show that the utilisation of generalised strategies in our experiments reduced the cost of the less resource-intensive model by nearly 30 percent on average.
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