面向复杂信息寻求的整体引导蒙特卡洛树搜索
Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking
AI总结：
本文提出整体引导蒙特卡洛树搜索（HG-MCTS），通过自适应清单与多视角奖励建模引导大语言模型搜索助手进行渐进式信息收集，在复杂信息寻求任务中实现更全面的知识获取和更准确的最终回答。
摘要：
在数字信息海量化的时代，可用信息的庞大数量与高度异质性给复杂信息寻求带来了显著挑战。用户经常面临涉及浏览海量且多样数据源的多步网络搜索任务。这种复杂性要求每一步都保持全面、准确且相关。然而，传统搜索方法往往难以在局部精确性的需求与整体理解所需的更广泛语境之间取得平衡，导致复杂查询的关键方面未被充分探索。在本文中，我们介绍了一个基于大语言模型（LLM）的搜索助手，该助手采用了一种新的信息寻求范式，即整体引导蒙特卡洛树搜索（HG-MCTS）。我们将任务重新表述为一个带有知识记忆的渐进式信息收集过程，并在MCTS中把自适应清单与多视角奖励建模结合起来。自适应清单提供显式子目标，以引导MCTS过程全面覆盖复杂的用户查询。同时，我们的多视角奖励建模同时提供探索奖励和检索奖励，以及跟踪已完成和剩余子目标的进度反馈，并随着树搜索的推进不断完善清单。通过在局部树扩展与全局引导之间取得平衡，HG-MCTS减少了搜索路径中的冗余，并确保复杂查询的所有关键方面都得到妥善处理。在真实世界的复杂信息寻求任务上进行的大量实验表明，与现有基线相比，HG-MCTS能够获取全面的知识集合，并给出更准确的最终回答。
英文摘要：
In the era of vast digital information, the sheer volume and heterogeneity of available information present significant challenges for intricate information seeking. Users frequently face multistep web search tasks that involve navigating vast and varied data sources. This complexity demands every step remains comprehensive, accurate, and relevant. However, traditional search methods often struggle to balance the need for localized precision with the broader context required for holistic understanding, leaving critical facets of intricate queries underexplored. In this paper, we introduce an LLM-based search assistant that adopts a new information seeking paradigm with holistically guided Monte Carlo tree search (HG-MCTS). We reformulate the task as a progressive information collection process with a knowledge memory and unite an adaptive checklist with multi-perspective reward modeling in MCTS. The adaptive checklist provides explicit sub-goals to guide the MCTS process toward comprehensive coverage of complex user queries. Simultaneously, our multi-perspective reward modeling offers both exploration and retrieval rewards, along with progress feedback that tracks completed and remaining sub-goals, refining the checklist as the tree search progresses. By striking a balance between localized tree expansion and global guidance, HG-MCTS reduces redundancy in search paths and ensures that all crucial aspects of an intricate query are properly addressed. Extensive experiments on real-world intricate information seeking tasks demonstrate that HG-MCTS acquires thorough knowledge collections and delivers more accurate final responses compared with existing baselines.
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。
