MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
(2407.20183)Abstract
Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Inspired by the remarkable progress of LLMs, 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.
Overview
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MindSearch is a multi-agent framework designed to mimic human cognitive processes for web-based information retrieval, comprising components like WebPlanner and WebSearcher.
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The system was evaluated using closed-set and open-set QA tasks, showcasing notable improvements in response quality over baseline methods and state-of-the-art applications.
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MindSearch holds theoretical and practical implications for enhancing AI's cognitive capabilities and efficiency in complex information retrieval scenarios, with potential future research directions focusing on refining context management and response factuality.
MindSearch: Mimicking Human Minds for Enhanced Information Seeking
The paper "MindSearch: Mimicking Human Minds Elicits Deep AI Searcher," authored by Zehui Chen et al., addresses the intricate task of information seeking and integration using the capabilities of LLMs and traditional search engines. It focuses on mitigating three main issues associated with current methods: complex request retrieval, dispersed information integration, and handling extended content in LLMs. The proposed solution revolves around MindSearch, a sophisticated multi-agent framework designed to replicate human cognitive processes for web-based information retrieval and synthesis.
Framework Overview
MindSearch is constructed with two main components: the WebPlanner and the WebSearcher. The WebPlanner is responsible for decomposing complex user queries into atomic sub-questions through a dynamic graph construction process. These sub-questions are then managed by the WebSearcher, which executes hierarchical information retrieval to collect pertinent data from the web.
The WebPlanner models the multi-step information seeking akin to human problem-solving, where initial questions are broken down into simpler, more manageable tasks. These tasks are represented as nodes in a directed acyclic graph (DAG), facilitating a structured and logical flow of information retrieval. By dynamically extending this graph based on search results, WebPlanner ensures a comprehensive exploration of the query's scope.
WebSearcher, on the other hand, performs detailed web searches and filters through massive volumes of web pages to extract valuable information. It employs a coarse-to-fine retrieval strategy, initially aggregating broad search results and then narrowing down to the most relevant sources. This approach significantly enhances the efficiency and accuracy of information integration.
Experimental Evaluation
The effectiveness of MindSearch was thoroughly evaluated on both closed-set and open-set QA tasks, using GPT-4o and InternLM2.5-7B models. The closed-set QA included datasets like Bamboogle, Musique, and HotpotQA, while the open-set QA comprised 100 real-world human queries assessed by expert human evaluators.
For closed-set QA tasks, MindSearch demonstrated a notable improvement over baseline methods, including raw LLMs without search engine integration and simple ReAct-style search interactions. The performance metrics indicated substantial gains in the quality of responses, with MindSearch excelling particularly in tasks requiring intricate reasoning and multi-hop queries.
In the open-set QA evaluations, MindSearch's responses were preferred by human evaluators over those from state-of-the-art applications like ChatGPT-Web and Perplexity.ai. The evaluators assessed the responses based on depth, breadth, and factuality. MindSearch outperformed its competitors significantly in terms of depth and breadth, indicating its ability to provide detailed and comprehensive answers. However, the factuality of responses remained a challenge, suggesting an area for further improvement in the integration of search results with LLM capabilities.
Theoretical and Practical Implications
The theoretical implications of MindSearch are profound, highlighting the potential of multi-agent frameworks to enhance the cognitive capabilities of AI systems. By distributing complex tasks across specialized agents, MindSearch effectively manages long-context scenarios and reduces the computational burden on individual components. This approach aligns with cognitive models of human problem-solving, where complex tasks are decomposed into simpler steps managed by different cognitive agents.
Practically, MindSearch offers a robust solution for AI-driven search engines, particularly in domains requiring extensive information retrieval and synthesis. The framework's ability to navigate and integrate content from over 300 web pages within minutes presents significant advantages for applications in research, education, and knowledge management.
Future Directions
Future research could focus on refining the context management among different agents within the MindSearch framework. Addressing the issue of factuality in responses is crucial to enhance the reliability of AI-driven searches further. Additionally, exploring the integration of reinforcement learning and behavior cloning techniques could provide more autonomy and efficiency in web automation tasks.
In conclusion, MindSearch presents a significant advancement in the field of AI-driven information seeking and integration. Its multi-agent framework not only enhances the depth and breadth of responses but also offers a scalable and efficient solution for complex cognitive tasks. This work is a pivotal step towards more sophisticated and human-like AI systems capable of tackling real-world challenges with precision and agility.
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