Synthetic Data Can Mislead Evaluations: Membership Inference as Machine Text Detection (2501.11786v1)

Abstract: Recent work shows membership inference attacks (MIAs) on LLMs produce inconclusive results, partly due to difficulties in creating non-member datasets without temporal shifts. While researchers have turned to synthetic data as an alternative, we show this approach can be fundamentally misleading. Our experiments indicate that MIAs function as machine-generated text detectors, incorrectly identifying synthetic data as training samples regardless of the data source. This behavior persists across different model architectures and sizes, from open-source models to commercial ones such as GPT-3.5. Even synthetic text generated by different, potentially larger models is classified as training data by the target model. Our findings highlight a serious concern: using synthetic data in membership evaluations may lead to false conclusions about model memorization and data leakage. We caution that this issue could affect other evaluations using model signals such as loss where synthetic or machine-generated translated data substitutes for real-world samples.

• The paper shows that membership inference attacks tend to detect machine-generated text instead of actual training membership.
• The replacement of human-written data with synthetic samples significantly lowers AUC metrics, compromising evaluation accuracy.
• The study calls for robust methods to differentiate between synthetic and genuine data to improve privacy and model evaluation protocols.

Membership Inference and the Misleading Nature of Synthetic Data

The paper "Synthetic Data Can Mislead Evaluations: Membership Inference as Machine Text Detection" addresses critical issues surrounding the use of synthetic data in membership inference attacks (MIAs) on LLMs. Authored by Ali Naseh and Niloofar Mireshghallah, the paper provides a detailed empirical analysis to demonstrate how synthetic data, commonly used as a stand-in for real non-member data, can skew the results of MIAs, hence potentially obfuscating our understanding of model memorization and privacy leakage.

Background and Motivation

Membership inference attacks are pivotal for assessing privacy vulnerabilities in LLMs by attempting to determine if a particular data sample was part of the training set. An accurate MIA can reveal a model's propensity for data memorization, which implicates privacy risks and legal concerns, such as copyright infringements. However, previous research has highlighted the limited success of current MIAs, which often perform only marginally better than random guesses when applied to major LLMs.

The paper identifies a significant challenge in MIA research: constructing representative non-member datasets free of temporal shifts. This difficulty has prompted the use of synthetic data; however, the authors contend that this approach introduces other critical flaws.

Experimental Findings

The authors' primary assertion is that MIAs inadvertently operate as detectors of machine-generated text rather than providing accurate membership predictions. In their experiments, MIAs consistently misclassify synthetic text as evidence of training membership, an outcome independent of the models generating the synthetic data or those being analyzed. Importantly, the abnormalities in membership inference metrics, such as AUC values dropping below random chance levels, suggest that synthetic text unduly influences MIA outputs by mimicking training data properties in the likelihood space.

Key experimental results demonstrate how the inclusion of synthetic data as non-member samples severely diminishes the AUC of MIAs. For example, substituting human-written non-members with synthetic ones generated by models like GPT-3.5 led to a decrease in the AUC from significant levels to well below 0.5. This finding illustrates that the MIAs not only misclassify synthetic texts but show a preference for them over actual training data, thus reversing their intended judgment criteria.

Implications

The implications of these findings are profound. The use of synthetic data invalidates the integrity of MIA results, thus misleading any subsequent privacy assessments that rely on such evaluations. This issue is further compounded by the cross-model applicability of these findings, indicating that regardless of the model architecture or size, synthetic data often confounds MIAs due to similar endogenous optimization patterns found in LLM probability spaces.

This paper calls into question numerous machine learning evaluation protocols that depend on synthetic datasets. It also challenges the reliability of benchmarks where machine-generated text is a commonplace substitute for unseen data, potentially biasing the metrics toward detecting artificial generative artifacts rather than genuine data properties.

Future Directions

The work opens several avenues for future research. First, developing robust MIA methodologies that do not confuse synthetic and actual training data is crucial. Such developments might involve advanced normalization techniques or innovative attack strategies that account for the nuanced differences between machine-generated and human-authored content. Additionally, investigating alternative non-member data collection frameworks that can ensure authenticity without succumbing to temporal or contextual shifts will be vital.

In a broader sense, the paper prompts further exploration of how LLMs' propensity to generate high-probability text influences evaluations and can artificially inflate security and privacy metrics. A more nuanced understanding of why models confuse synthetic text with training samples could illuminate systemic biases inherent in neural models' learning processes.

Conclusion

This paper serves as a critical examination of the methodological flaws in using synthetic data for evaluating membership inference attacks on LLMs. It underscores a significant risk: that the prevalent use of synthetic data could lead to fundamentally flawed conclusions about AI model privacy and memorization effects. By bringing these issues to light, the research advocates for a reevaluation of current practices and strategizes towards more reliable methodologies for the future development and assessment of machine learning systems.