Holistically Guided Monte Carlo Tree Search for Intricate Information Seeking
Ruiyang Ren, Yuhao Wang, Junyi Li, Jinhao Jiang, Wayne Xin Zhao,, Wenjie Wang, Tat-Seng Chua

TL;DR
This paper presents HG-MCTS, a novel search method combining holistic guidance and Monte Carlo tree search to improve complex information retrieval, ensuring comprehensive and accurate responses in multi-step web search tasks.
Contribution
The paper introduces a new HG-MCTS framework that integrates an adaptive checklist and multi-perspective reward modeling for improved intricate information seeking.
Findings
HG-MCTS outperforms existing methods in accuracy and coverage.
The approach effectively balances local exploration and global guidance.
Experimental results show enhanced knowledge collection and response quality.
Abstract
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…
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Taxonomy
TopicsData Management and Algorithms · Artificial Intelligence in Games · Advanced Database Systems and Queries
