An Automated Hierarchy Method to Improve History Record Accessibility in Text-to-Image Generative AI

This study aims to enhance access to historical records by improving the efficiency of record retrieval in generative AI, which is increasingly utilized across various fields for generating visual content and gaining inspiration due to its ease of use. Currently, most generative AIs, such as Dall-E and Midjourney, employ conversational user interfaces (CUIs) for content creation and record retrieval. While CUIs facilitate natural interactions between complex AI models and users by making the creation process straightforward, they have limitations when it comes to navigating past records. Specifically, CUIs require numerous interactions, and users must sift through unnecessary information to find desired records, a challenge that intensifies as the volume of information grows. To address these limitations, we propose an automatic hierarchy method. This method, considering the modality characteristics of text-to-image applications, is implemented with two approaches: vision-based (output images) and prompt-based (input text) approaches. To validate the effectiveness of the automatic hierarchy method and assess the impact of these two approaches on users, we conducted a user study with 12 participants. The results indicated that the automatic hierarchy method enables more efficient record retrieval than traditional CUIs, and user preferences between the two approaches varied depending on their work patterns. This study contributes to overcoming the limitations of linear record retrieval in existing CUI systems through the development of an automatic hierarchy method. It also enhances record retrieval accessibility, which is essential for generative AI to function as an effective tool, and suggests future directions for research in this area.