用专家角色框架让大模型跨学科生成实用创新方案
Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devices
- 构建多维度专家代理，按分类原则系统生成解决方案
- 在OLED和新型电极设计中显著提升有效解产出率
- 适合需要跨学科突破的科研与工程研发人员
近期研究越来越多地探索利用大语言模型（LLMs）生成研究思路与科学假设。然而，现实中的研发常面临复杂且跨学科的挑战，现有知识难以直接提供有效解。因此，有必要借助LLMs中蕴含的广泛知识，通过整合多领域视角生成突破性解决方案。本文提出SELLM（Solution Enumeration via comprehensive List and LLM）框架，利用LLMs结合MECE（互斥且穷尽）原则，如国际专利分类（IPC）和元素周期表，系统构建综合性专家代理，生成跨学科、高效的解决方案。为评估其实用性，我们将其应用于两个挑战：提升有机发光二极管（OLED）照明中的光提取效率，以及开发下一代存储材料的电极。结果表明，相较于无定制化或未投入精力的情况，SELLM显著提升了有效解决方案的生成能力，展现了其在应对复杂问题时赋予大模型生成有效解的潜力。
原文摘要 · Abstract (English)
Recently, many studies have increasingly explored the use of large language models (LLMs) to generate research ideas and scientific hypotheses. However, real-world research and development often require solving complex, interdisciplinary challenges where solutions may not be readily found through existing knowledge related to the problem. Therefore, it is desirable to leverage the vast, comprehensive knowledge of LLMs to generate effective, breakthrough solutions by integrating various perspectives from other disciplines. Here, we propose SELLM (Solution Enumeration via comprehensive List and LLM), a framework leveraging LLMs and structured guidance using MECE (Mutually Exclusive, Collectively Exhaustive) principles, such as International Patent Classification (IPC) and the periodic table of elements. SELLM systematically constructs comprehensive expert agents from the list to generate cross-disciplinary and effective solutions. To evaluate SELLM's practicality, we applied it to two challenges: improving light extraction in organic light-emitting diode (OLED) lighting and developing electrodes for next-generation memory materials. The results demonstrate that SELLM significantly facilitates the generation of effective solutions compared to cases without specific customization or effort, showcasing the potential of SELLM to enable LLMs to generate effective solutions even for challenging problems.
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