Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913859818749952 |
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| author | Su, Haoyang Chen, Renqi Tang, Shixiang Yin, Zhenfei Zheng, Xinzhe Li, Jinzhe Qi, Biqing Wu, Qi Li, Hui Ouyang, Wanli Torr, Philip Zhou, Bowen Dong, Nanqing |
| author_facet | Su, Haoyang Chen, Renqi Tang, Shixiang Yin, Zhenfei Zheng, Xinzhe Li, Jinzhe Qi, Biqing Wu, Qi Li, Hui Ouyang, Wanli Torr, Philip Zhou, Bowen Dong, Nanqing |
| contents | The rapid advancement of scientific progress requires innovative tools that can accelerate knowledge discovery. Although recent AI methods, particularly large language models (LLMs), have shown promise in tasks such as hypothesis generation and experimental design, they fall short of replicating the collaborative nature of real-world scientific practices, where diverse experts work together in teams to tackle complex problems. To address the limitations, we propose an LLM-based multi-agent system, i.e., Virtual Scientists (VirSci), designed to mimic the teamwork inherent in scientific research. VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas. Through comprehensive experiments, we demonstrate that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas. We further investigate the collaboration mechanisms that contribute to its tendency to produce ideas with higher novelty, offering valuable insights to guide future research and illuminating pathways toward building a robust system for autonomous scientific discovery. The code is available at https://github.com/open-sciencelab/Virtual-Scientists. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09403 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System Su, Haoyang Chen, Renqi Tang, Shixiang Yin, Zhenfei Zheng, Xinzhe Li, Jinzhe Qi, Biqing Wu, Qi Li, Hui Ouyang, Wanli Torr, Philip Zhou, Bowen Dong, Nanqing Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning Multiagent Systems The rapid advancement of scientific progress requires innovative tools that can accelerate knowledge discovery. Although recent AI methods, particularly large language models (LLMs), have shown promise in tasks such as hypothesis generation and experimental design, they fall short of replicating the collaborative nature of real-world scientific practices, where diverse experts work together in teams to tackle complex problems. To address the limitations, we propose an LLM-based multi-agent system, i.e., Virtual Scientists (VirSci), designed to mimic the teamwork inherent in scientific research. VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas. Through comprehensive experiments, we demonstrate that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas. We further investigate the collaboration mechanisms that contribute to its tendency to produce ideas with higher novelty, offering valuable insights to guide future research and illuminating pathways toward building a robust system for autonomous scientific discovery. The code is available at https://github.com/open-sciencelab/Virtual-Scientists. |
| title | Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System |
| topic | Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2410.09403 |