Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text
This work addresses improving user experience in data tools by personalizing outputs based on user characteristics, with incremental insights into LLM applications for preference modeling.
The study investigated user preferences for data presentation formats (charts, tables, or text) based on questions, finding that personal traits influence these preferences, and explored the use of LLMs to replicate user preferences with and without user data.
In this work, we research user preferences to see a chart, table, or text given a question asked by the user. This enables us to understand when it is best to show a chart, table, or text to the user for the specific question. For this, we conduct a user study where users are shown a question and asked what they would prefer to see and used the data to establish that a user's personal traits does influence the data outputs that they prefer. Understanding how user characteristics impact a user's preferences is critical to creating data tools with a better user experience. Additionally, we investigate to what degree an LLM can be used to replicate a user's preference with and without user preference data. Overall, these findings have significant implications pertaining to the development of data tools and the replication of human preferences using LLMs. Furthermore, this work demonstrates the potential use of LLMs to replicate user preference data which has major implications for future user modeling and personalization research.