通过REAP（反思、显式问题解构与高级提示）增强大语言模型的问题求解能力
Enhancing LLM Problem Solving with REAP: Reflection, Explicit Problem Deconstruction, and Advanced Prompting
AI总结：
针对大语言模型复杂推理任务求解能力不足的问题，提出包含反思、显式问题解构与高级提示的REAP方法，在多个前沿模型上实现显著性能提升，同时兼具成本效益与输出可解释性优势。
摘要：
大语言模型（LLM）已革新自然语言处理领域，但提升其问题求解能力——尤其是针对复杂的、强推理任务——仍是一项长期挑战。本文提出REAP（反思、显式问题解构与高级提示）方法，这是动态上下文生成框架下的一种创新方案。REAP引导大语言模型先对查询进行反思，将其拆解为可处理的组件，再生成相关上下文以优化求解过程。我们使用一个专门用于暴露LLM局限性的数据集评估REAP，在6个前沿模型（OpenAI的o1-preview、o1-mini、GPT-4o、GPT-4o-mini，谷歌的Gemini 1.5 Pro，以及Claude 3.5 Sonnet）上对比了零样本提示与REAP增强提示的效果。结果显示性能提升显著：o1-mini提升40.97%，GPT-4o提升66.26%，GPT-4o-mini提升112.93%。尽管OpenAI的o1-preview基线性能已很强，仍观测到了小幅提升。除性能提升外，REAP还具备成本效益：例如，GPT-4o-mini的成本约为o1-preview的1/100，却能产出具有竞争力的结果。REAP还提升了模型输出的清晰度，让人类更容易理解结果背后的推理过程，也简化了问题识别与修正的流程。这些发现表明，REAP具备大幅提升LLM能力的潜力，可在广泛应用中同时实现更优性能与更高成本效率。
英文摘要：
Large Language Models (LLMs) have transformed natural language processing, yet improving their problem-solving capabilities, particularly for complex, reasoning-intensive tasks, remains a persistent challenge. This paper introduces the REAP (Reflection, Explicit Problem Deconstruction, and Advanced Prompting) method, an innovative approach within the dynamic context generation framework. REAP guides LLMs through reflection on the query, deconstructing it into manageable components, and generating relevant context to enhance the solution process. We evaluated REAP using a dataset designed to expose LLM limitations, comparing zero-shot prompting with REAP-enhanced prompts across six state-of-the-art models: OpenAI's o1-preview, o1-mini, GPT-4o, GPT-4o-mini, Google's Gemini 1.5 Pro, and Claude 3.5 Sonnet. The results demonstrate notable performance gains, with o1-mini improving by 40.97%, GPT-4o by 66.26%, and GPT-4o-mini by 112.93%. Despite the already strong baseline performance of OpenAI's o1-preview, modest gains were observed. Beyond performance improvements, REAP offers a cost-effective solution; for example, GPT-4o-mini, which is approximately 100 times cheaper than o1-preview, delivered competitive results. REAP also improves the clarity of model outputs, making it easier for humans to understand the reasoning behind the results and simplifying the process of identifying and addressing any issues. These findings demonstrate REAP's potential to greatly improve the capabilities of LLMs, providing both better performance and increased cost-efficiency across a wide range of applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
