Summary of Mindsearch: Mimicking Human Minds Elicits Deep Ai Searcher, by Zehui Chen et al.
MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
by Zehui Chen, Kuikun Liu, Qiuchen Wang, Jiangning Liu, Wenwei Zhang, Kai Chen, Feng Zhao
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Inspired by Large Language Models (LLMs), recent works aim to solve complex cognitive tasks like information seeking and integration. However, current methods still struggle due to three main challenges: complex requests are difficult to retrieve accurately, corresponding information is spread across multiple web pages with massive noise, and LLMs have limited context lengths. The authors introduce MindSearch, a framework that mimics human cognition in web information seeking and integration using an LLM-based multi-agent approach. WebPlanner models the human mind as a dynamic graph construction process, decomposing queries into atomic sub-questions and extending the graph based on search results from WebSearcher. Tasked with each sub-question, WebSearcher performs hierarchical information retrieval, collecting valuable information for WebPlanner. MindSearch demonstrates significant improvements in response quality on both close-set and open-set QA problems, outperforming proprietary AI search engines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find specific information online takes a lot of time and effort. Recently, people have tried combining powerful language models with search engines to make this task easier. However, these attempts still struggle due to three main challenges: complex requests are hard to understand, relevant information is spread across many web pages, and the language models can only handle so much information at once. To solve this problem, researchers created a new system called MindSearch that mimics how humans find and combine information online. MindSearch uses multiple agents working together to quickly find and integrate information from hundreds of web pages in just a few minutes. This is much faster than what humans can do! The results show that MindSearch can provide better answers to questions than some proprietary AI search engines. |
