通过迭代检索外部知识，让大模型生成更多新颖多样科研想法。
Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas
- 迭代规划外部知识检索，逐步深化创意生成。
- 生成的独特新想法数量是原有方法的3.4倍。
- 在170篇论文评估中，优质想法数量超当前最佳方法2.5倍。
科学创新对人类至关重要，利用大语言模型（LLMs）生成研究想法可推动发现进程。然而，现有LLMs因缺乏获取外部知识的能力，常产生简单重复的建议。为此，我们提出一种增强型规划与搜索方法，通过迭代过程主动规划外部知识的检索，逐步丰富创意生成的广度与深度。自动化与人工评估验证表明，该框架显著提升了生成想法的质量，尤其在新颖性和多样性方面。在相同条件下，本方法产生的独特新想法数量为无该机制时的3.4倍；在基于170篇种子论文的瑞士淘汰赛评估中，生成的优质想法数量至少是当前最先进方法的2.5倍。
原文摘要 · Abstract (English)
Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability in acquiring external knowledge for innovation. To address this problem, we introduce an enhanced planning and search methodology designed to boost the creative potential of LLM-based systems. Our approach involves an iterative process to purposely plan the retrieval of external knowledge, progressively enriching the idea generation with broader and deeper insights. Validation through automated and human assessments indicates that our framework substantially elevates the quality of generated ideas, particularly in novelty and diversity. The number of unique novel ideas produced by our framework is 3.4 times higher than without it. Moreover, our method outperforms the current state-of-the-art, generating at least 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation.
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