An Automatic Bottom-up Idea Grouping Method based on LLMs

Abstract

This paper presents a novel automatic Bottom-up idea grouping method leveraging Large Language Models (LLMs) to facilitate the organization of ideas and concepts. Traditional methods of idea grouping, such as the KJ method, require significant manual effort to group and synthesize information effectively. Our approach utilizes the contextual understanding and grouping capabilities of LLMs to automate this process, aiming to reduce cognitive load and improve efficiency. By performing unsupervised grouping based on the insight of each idea, the model automatically generates cohesive idea groups from diverse sets of inputs in the bottom-up way. We evaluate the effectiveness of our method on qualitative datasets, comparing it with top-down categorizing method. Results indicate that the proposed bottom-up method not only aligns well with human clustering but also demonstrates a high level of interpretability and accuracy in grouping similar ideas. This study highlights the potential of LLMs in transforming qualitative analysis by offering a scalable and intuitive solution for idea grouping.