A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models

2024-04-20

arXiv.org

AI Summary

Existing research lacks a unified, quantitative framework to simultaneously evaluate the quality, downstream utility, and privacy preservation capabilities of large language model (LLM)-generated structured synthetic data—e.g., product reviews. Method: We propose SynEval, an open-source evaluation framework that introduces the first tri-dimensional quantitative paradigm integrating fidelity (statistical similarity), utility (performance on downstream ML tasks), and privacy (robustness against membership and attribute inference attacks). Contribution/Results: SynEval systematically characterizes inherent trade-offs among these dimensions through rigorous statistical analysis, task-based benchmarking, and adversarial privacy testing. Empirically validated on synthetic reviews generated by ChatGPT, Claude, and Llama, it delivers reproducible, interpretable insights for synthetic data selection and deployment—enabling principled, evidence-based decision-making in real-world applications.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The rapid advancements in generative AI and large language models (LLMs) have opened up new avenues for producing synthetic data, particularly in the realm of structured tabular formats, such as product reviews. Despite the potential benefits, concerns regarding privacy leakage have surfaced, especially when personal information is utilized in the training datasets. In addition, there is an absence of a comprehensive evaluation framework capable of quantitatively measuring the quality of the generated synthetic data and their utility for downstream tasks. In response to this gap, we introduce SynEval, an open-source evaluation framework designed to assess the fidelity, utility, and privacy preservation of synthetically generated tabular data via a suite of diverse evaluation metrics. We validate the efficacy of our proposed framework - SynEval - by applying it to synthetic product review data generated by three state-of-the-art LLMs: ChatGPT, Claude, and Llama. Our experimental findings illuminate the trade-offs between various evaluation metrics in the context of synthetic data generation. Furthermore, SynEval stands as a critical instrument for researchers and practitioners engaged with synthetic tabular data,, empowering them to judiciously determine the suitability of the generated data for their specific applications, with an emphasis on upholding user privacy.

Problem

• Lack of comprehensive framework for synthetic data evaluation
• Privacy concerns in synthetic data generation using LLMs
• Need to assess fidelity, utility, and privacy of synthetic data

Innovation

• Open-source framework SynEval for synthetic data
• Evaluates fidelity, utility, and privacy metrics
• Validated with ChatGPT, Claude, Llama outputs