Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications

2025-02-02

AI Summary

Large language models (LLMs) generating synthetic data introduce bias amplification and hallucination risks when integrated into downstream tools—e.g., classifiers, generators, and visualization systems. Method: This paper proposes the first black-box auditing paradigm specifically designed for synthetic artifacts, requiring no access to model architecture, training data, or parameters. Leveraging only ~200 queries on average, it integrates three disclosure-free, cross-modal, and cross-task auditing strategies: statistical bias measurement, lightweight fine-tuning probes, and supervised classification. Contribution/Results: Evaluated across three text classification, two summarization, and two visualization tasks, the method achieves mean audit accuracy of 0.868–0.880, demonstrating strong effectiveness, practicality, and generalizability in identifying downstream dependence on synthetic data.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Large language models (LLMs) have facilitated the generation of high-quality, cost-effective synthetic data for developing downstream models and conducting statistical analyses in various domains. However, the increased reliance on synthetic data may pose potential negative impacts. Numerous studies have demonstrated that LLM-generated synthetic data can perpetuate and even amplify societal biases and stereotypes, and produce erroneous outputs known as ``hallucinations'' that deviate from factual knowledge. In this paper, we aim to audit artifacts, such as classifiers, generators, or statistical plots, to identify those trained on or derived from synthetic data and raise user awareness, thereby reducing unexpected consequences and risks in downstream applications. To this end, we take the first step to introduce synthetic artifact auditing to assess whether a given artifact is derived from LLM-generated synthetic data. We then propose an auditing framework with three methods including metric-based auditing, tuning-based auditing, and classification-based auditing. These methods operate without requiring the artifact owner to disclose proprietary training details. We evaluate our auditing framework on three text classification tasks, two text summarization tasks, and two data visualization tasks across three training scenarios. Our evaluation demonstrates the effectiveness of all proposed auditing methods across all these tasks. For instance, black-box metric-based auditing can achieve an average accuracy of $0.868 pm 0.071$ for auditing classifiers and $0.880 pm 0.052$ for auditing generators using only 200 random queries across three scenarios. We hope our research will enhance model transparency and regulatory compliance, ensuring the ethical and responsible use of synthetic data.

Problem

• Bias Amplification
• Fake Data
• Large Language Models

Innovation

• Data Integrity
• Bias Mitigation
• Ethical AI