Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devices
This addresses the problem of leveraging LLMs for breakthrough solutions in real-world, interdisciplinary R&D, though it appears incremental as it builds on existing LLM methods with structured guidance.
The paper tackles the challenge of generating effective, interdisciplinary solutions for complex R&D problems by proposing SELLM, a framework that uses LLMs with structured guidance like IPC and the periodic table to create comprehensive expert agents. Results show SELLM significantly facilitates solution generation for improving OLED lighting and developing memory electrodes compared to non-customized approaches.
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.