SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains

2025-07-09

AI Summary

Systematic evaluation of synthetic text quality remains underdeveloped in high-stakes domains such as healthcare and law, where functional utility, safety, and domain-specific expertise are critical. Method: This paper introduces the first multidimensional evaluation framework tailored for high-risk domains, unifying quantification across three dimensions: functionality (downstream task performance), safety (privacy leakage risk and distributional fidelity), and professionalism (fairness and expert credibility). The framework integrates large language model generation, distributional divergence analysis, privacy leakage detection algorithms, fairness metrics, and an expert feedback interface to enable synergistic automated assessment and human validation. Contribution/Results: Experiments on real-world medical and legal datasets demonstrate that the framework significantly enhances the ability of synthetic texts to preserve semantic and statistical properties while substantially reducing privacy risks. It establishes a reproducible, scalable, and privacy-preserving evaluation paradigm for responsible AI development in sensitive applications.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

We present SynthTextEval, a toolkit for conducting comprehensive evaluations of synthetic text. The fluency of large language model (LLM) outputs has made synthetic text potentially viable for numerous applications, such as reducing the risks of privacy violations in the development and deployment of AI systems in high-stakes domains. Realizing this potential, however, requires principled consistent evaluations of synthetic data across multiple dimensions: its utility in downstream systems, the fairness of these systems, the risk of privacy leakage, general distributional differences from the source text, and qualitative feedback from domain experts. SynthTextEval allows users to conduct evaluations along all of these dimensions over synthetic data that they upload or generate using the toolkit's generation module. While our toolkit can be run over any data, we highlight its functionality and effectiveness over datasets from two high-stakes domains: healthcare and law. By consolidating and standardizing evaluation metrics, we aim to improve the viability of synthetic text, and in-turn, privacy-preservation in AI development.

Problem

• Evaluating synthetic text utility in high-stakes domains
• Assessing privacy risks in AI-generated synthetic data
• Standardizing metrics for synthetic text quality evaluation

Innovation

• Toolkit for synthetic text evaluation
• Multi-dimensional synthetic data assessment
• Privacy-preserving AI development support