We present a novel approach to exploring innovation problem and solution domains using LLM fine-tuning with a custom idea database. By semantically traversing the bi-directional problem and solution tree at different temperature levels we achieve high diversity in solution edit distance while still remaining close to the original problem statement semantically. In addition to finding a variety of solutions to a given problem, this method can also be used to refine and clarify the original problem statement. As further validation of the approach, we implemented a proof-of-concept Slack bot to serve as an innovation assistant.

翻译：我们提出了一种新颖方法，通过使用自定义创意数据库对大型语言模型进行微调来探索创新问题与解空间。通过在不同温度层级上对双向问题-解树进行语义遍历，我们能够在保持与原始问题描述语义相近的同时，实现解编辑距离的高度多样性。此方法不仅能针对给定问题寻找多样化解决方案，还可用于优化与澄清原始问题陈述。为验证该方法的有效性，我们开发了一款概念验证型Slack机器人作为创新助手。