How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilities

Fuente: arXiv

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Bibliographic Details
Main Authors:	Lin, Minhua, Liu, Hui, Tang, Xianfeng, Zeng, Jingying, Dai, Zhenwei, Luo, Chen, Li, Zheng, Zhang, Xiang, He, Qi, Wang, Suhang
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
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_version_	1866929732198596608
author	Lin, Minhua Liu, Hui Tang, Xianfeng Zeng, Jingying Dai, Zhenwei Luo, Chen Li, Zheng Zhang, Xiang He, Qi Wang, Suhang
author_facet	Lin, Minhua Liu, Hui Tang, Xianfeng Zeng, Jingying Dai, Zhenwei Luo, Chen Li, Zheng Zhang, Xiang He, Qi Wang, Suhang
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2502_18387
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilities Lin, Minhua Liu, Hui Tang, Xianfeng Zeng, Jingying Dai, Zhenwei Luo, Chen Li, Zheng Zhang, Xiang He, Qi Wang, Suhang Artificial Intelligence 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.
title	How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilities
topic	Artificial Intelligence
url	https://arxiv.org/abs/2502.18387