用大模型提升搜索效率，减少99.1%搜索空间。
How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilities
- 用大模型指导搜索，构建SeaL框架加速求解。
- 在真实规划任务中达近完美准确率，搜索空间缩小99.1%。
- 适合想提升大模型问题求解能力的研究者。
搜索在各类领域的问题求解中具有基础作用，多数现实决策问题可通过系统性搜索解决。受近期关于搜索与学习关系的启发，本文从三个维度系统探讨搜索与大语言模型（LLMs）的互补性。首先分析学习如何提升搜索效率，提出基于LLMs的高效搜索框架SeaL；其次将SeaL扩展为SeaL-C，确保搜索过程的严格完备性。在三个真实世界规划任务上的评估表明，SeaL实现近似完美准确率，同时相比传统方法将搜索空间缩小高达99.1%。最后，通过分析当前LLMs是否能独立发展搜索能力，发现其在复杂问题中仍难以高效搜索，但引入系统性搜索策略可显著增强其求解能力。研究验证了所提方法的有效性，并强调提升LLMs搜索能力对实际应用的重要性。
原文摘要 · Abstract (English)
Search plays a fundamental role in problem-solving across various domains, with most real-world decision-making problems being solvable through systematic search. Drawing inspiration from recent discussions on search and learning, we systematically explore the complementary relationship between search and Large Language Models (LLMs) from three perspectives. First, we analyze how learning can enhance search efficiency and propose Search via Learning (SeaL), a framework that leverages LLMs for effective and efficient search. Second, we further extend SeaL to SeaL-C to ensure rigorous completeness during search. Our evaluation across three real-world planning tasks demonstrates that SeaL achieves near-perfect accuracy while reducing search spaces by up to 99.1% compared to traditional approaches. Finally, we explore how far LLMs are from real search by investigating whether they can develop search capabilities independently. Our analysis reveals that while current LLMs struggle with efficient search in complex problems, incorporating systematic search strategies significantly enhances their problem-solving capabilities. These findings not only validate the effectiveness of our approach but also highlight the need for improving LLMs' search abilities for real-world applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。