# Utilizing Large Language Models to Synthesize Product Desirability Datasets

arXiv: https://arxiv.org/abs/2411.13485 · HTML: https://mlsanity.com/paper/2411.13485/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Standardizing the Measurement of Text Diversity: A Tool and a Comparative Analysis of Scores](https://mlsanity.com/paper/2403.00553/) (2024)
- [Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA](https://mlsanity.com/paper/2403.16385/) (2024)
- [Using LLMs to Establish Implicit User Sentiment of Software Desirability](https://mlsanity.com/paper/2408.01527/) (2024)

## Built on by (most cited first, 4 of 4)

- [Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMs](https://mlsanity.com/paper/2502.07045/) (2025)
- [Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement](https://mlsanity.com/paper/2505.19675/) (2025)
- [An Ethically Grounded LLM-Based Approach to Insider Threat Synthesis and Detection](https://mlsanity.com/paper/2509.06920/) (2025)
- [Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability](https://mlsanity.com/paper/2606.23701/) (2026)

## Abstract (arXiv)

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.