Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (2505.05084v2)

Abstract: The rapid advancement of LLMs has raised significant concerns regarding their potential misuse by malicious actors. As a result, developing effective detectors to mitigate these risks has become a critical priority. However, most existing detection methods focus excessively on detection accuracy, often neglecting the societal risks posed by high false positive rates (FPRs). This paper addresses this issue by leveraging Conformal Prediction (CP), which effectively constrains the upper bound of FPRs. While directly applying CP constrains FPRs, it also leads to a significant reduction in detection performance. To overcome this trade-off, this paper proposes a Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (MCP), which both enforces the FPR constraint and improves detection performance. This paper also introduces RealDet, a high-quality dataset that spans a wide range of domains, ensuring realistic calibration and enabling superior detection performance when combined with MCP. Empirical evaluations demonstrate that MCP effectively constrains FPRs, significantly enhances detection performance, and increases robustness against adversarial attacks across multiple detectors and datasets.

Reliable False Positive Bounding in Machine-Generated Text Detection

In the contemporary landscape shaped by the rapid growth of LLMs, the detection of machine-generated text (MGT) has emerged as a critical task. Malicious actors increasingly exploit LLMs to create fake news, spam, and harmful content, which underscores the necessity of robust detection systems. While current approaches often prioritize detection accuracy, they frequently overlook false positive rates (FPRs), which can have detrimental societal impacts.

The paper "Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction" offers a comprehensive solution to control FPRs while maintaining strong detection performance. It introduces a novel framework that integrates conformal prediction (CP) principles, traditionally used to provide statistical guarantees, into the field of text detection. The proposed method, Multiscaled Conformal Prediction (MCP), effectively balances the trade-off between FPR constraints and detection accuracy, a challenge commonly observed with conventional CP applications.

Key Contributions and Methodology

The paper presents several notable contributions:

1. Integration of CP into MGT Detection: This is the first endeavor to apply CP in the context of machine-generated text detection, emphasizing the necessity to mitigate high FPRs and thereby reduce societal harm.
2. Development of MCP Framework: The MCP framework employs a zero-shot detection method that enhances robustness and performance without additional training, achieving effective FPR control.
3. RealDet Dataset Introduction: The creation of RealDet, a large-scale bilingual dataset with 847k raw texts spanning multiple domains, serves as a benchmark to test and calibrate detection systems realistically.

The MCP framework operates through a systematic process of data preparation, nonconformity score definition, multiscaled quantiles calculation, and MGT detection. Each step is designed to optimize the calibration of detection thresholds based on distinct text length intervals, thereby addressing inherent biases introduced by uniform CP quantile applications.

Empirical Evaluations and Implications

Extensive experimental evaluations demonstrate that MCP consistently constrains FPRs within predefined bounds across various detectors and datasets. It markedly enhances detection robustness, particularly in adversarial settings, which commonly degrade the efficacy of traditional methods. These results underscore its practical applicability in real-world scenarios demanding stringent reliability criteria.

Future Directions and Theoretical Insights

The paper posits potential improvements via adaptive binning strategies, suggesting that fixed-width binning might limit optimal calibration. Further exploration into customized bin intervals could refine detection precision.

The theoretical implications extend the applicability of conformal prediction beyond its conventional uses, opening avenues for advanced statistical learning frameworks in AI detection systems. The reliable bounding of FPRs promises significant advancements in the deployment of AI models where social responsibility and accuracy are paramount.

Conclusion

This paper represents a substantive advancement in MGT detection by strategically controlling false positives through the MCP framework. It addresses the urgent need for reliable detection systems amidst increasing reliance on LLMs, advocating for more responsible AI applications. Future research should focus on refining binning strategies and extending conformal prediction principles to other AI domains, ensuring continued innovation and evaluation under varied real-world conditions.