心靈搜尋：模仿人類思維引發深度人工智慧搜索者

摘要

資訊尋求和整合是一項複雜的認知任務，耗費大量時間和精力。受到大型語言模型顯著進展的啟發，最近的研究嘗試通過結合大型語言模型和搜索引擎來解決這個任務。然而，由於三個挑戰，這些方法仍然無法獲得令人滿意的性能：(1) 複雜的請求往往無法被搜索引擎準確和完整地檢索一次，(2) 要整合的相應信息分散在多個網頁上，並伴隨著大量噪音，(3) 大量內容豐富的網頁可能很快就會超過大型語言模型的最大上下文長度。受到人類解決這些問題時的認知過程的啟發，我們引入MindSearch來模擬人類在網絡信息尋求和整合中的思維，這可以通過一個簡單而有效的基於大型語言模型的多代理框架來實現。WebPlanner將多步信息尋求的人類思維建模為一個動態圖構建過程：它將用戶查詢分解為圖中的原子子問題節點，並根據WebSearcher的搜索結果逐步擴展圖。WebSearcher負責每個子問題，它通過搜索引擎執行分層信息檢索並為WebPlanner收集有價值的信息。MindSearch的多代理設計使整個框架能夠在3分鐘內並行地從規模更大的網頁（例如超過300個）中尋求和整合信息，相當於人類努力3小時的價值。MindSearch在深度和廣度方面顯著提高了回應質量，無論是在閉集還是開集的問答問題上。此外，基於InternLM2.5-7B的MindSearch的回應比ChatGPT-Web和Perplexity.ai應用更受人類青睞，這表明MindSearch已經能夠為專有AI搜索引擎提供具有競爭力的解決方案。

English

Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Inspired by the remarkable progress of Large Language Models, recent works attempt to solve this task by combining LLMs and search engines. However, these methods still obtain unsatisfying performance due to three challenges: (1) complex requests often cannot be accurately and completely retrieved by the search engine once (2) corresponding information to be integrated is spread over multiple web pages along with massive noise, and (3) a large number of web pages with long contents may quickly exceed the maximum context length of LLMs. Inspired by the cognitive process when humans solve these problems, we introduce MindSearch to mimic the human minds in web information seeking and integration, which can be instantiated by a simple yet effective LLM-based multi-agent framework. The WebPlanner models the human mind of multi-step information seeking as a dynamic graph construction process: it decomposes the user query into atomic sub-questions as nodes in the graph and progressively extends the graph based on the search result from WebSearcher. Tasked with each sub-question, WebSearcher performs hierarchical information retrieval with search engines and collects valuable information for WebPlanner. The multi-agent design of MindSearch enables the whole framework to seek and integrate information parallelly from larger-scale (e.g., more than 300) web pages in 3 minutes, which is worth 3 hours of human effort. MindSearch demonstrates significant improvement in the response quality in terms of depth and breadth, on both close-set and open-set QA problems. Besides, responses from MindSearch based on InternLM2.5-7B are preferable by humans to ChatGPT-Web and Perplexity.ai applications, which implies that MindSearch can already deliver a competitive solution to the proprietary AI search engine.

心靈搜尋：模仿人類思維引發深度人工智慧搜索者

MindSearch: Mimicking Human Minds Elicits Deep AI Searcher

摘要

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