Using LLMs to Synthesize Product Desirability Datasets

Authors

John Hastings, Dakota State UniversityFollow
Sherri Weitl-Harms, Creighton UniversityFollow
Joseph Doty, Dakota State UniversityFollow
Zachary J. Myers, Dakota State UniversityFollow
Warren Thompson, Dakota State UniversityFollow

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Description

This research investigates the use of large language models (LLMs) to generate synthetic datasets for the evaluation of product desirability using the Product Desirability Toolkit (PDT). The study aims to identify if LLMs can effectively create cost-efficient and scalable datasets to enhance sentiment analysis and user experience design.

Publication Date

2025

Recommended Citation

Hastings, John; Weitl-Harms, Sherri; Doty, Joseph; Myers, Zachary J.; and Thompson, Warren, "Using LLMs to Synthesize Product Desirability Datasets" (2025). Annual Research Symposium. 57.
https://scholar.dsu.edu/research-symposium/57