Dashboard Filtering Using LLM-Based Interfaces

Abstract

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Data dashboards can visually present data for filtering, interaction and data analysis in a dynamic and intuitive way. In the automotive sector, test engineers are interested in monitoring and analyzing vehicle measurement data from test drives. Data sections recorded under specific conditions are especially relevant, showing important scenarios of the driving operation. This work explores Large Language Models (LLM) assisting in data filtering requests that could help the user identify relevant dashboard filter settings for their domain-specific questions. We use function calling to select functions and corresponding parameter values from a provided set of analysis functions. We tested our method using GPT-3.5- Turbo and zero-shot generalization with a generated test data set of user prompts. The results show that correct functions and parameters can be selected by the model, but iterative and use case-specific adaptations and fine-tuning might be required to achieve a reliable automatic functionality in a dashboard application. We propose possible further approaches, with user studies being relevant for the exploration of productive usage.