Outlet Title
2024 IEEE International Conference on Big Data (BigData)
Document Type
Conference Proceeding
Publication Date
Winter 12-15-2024
Abstract
This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production.
Recommended Citation
Hastings, John D.; Weitl-Harms, Sherri; Doty, Joseph; Myers, Zachary L.; and Thompson, Warren, "Utilizing Large Language Models to Synthesize Product Desirability Datasets" (2024). Research & Publications. 83.
https://scholar.dsu.edu/ccspapers/83
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Marketing Commons, Technology and Innovation Commons