Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature (2310.05130v3)

Abstract: LLMs have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks. To build trustworthy AI systems, it is imperative to distinguish between machine-generated and human-authored content. The leading zero-shot detector, DetectGPT, showcases commendable performance but is marred by its intensive computational costs. In this paper, we introduce the concept of conditional probability curvature to elucidate discrepancies in word choices between LLMs and humans within a given context. Utilizing this curvature as a foundational metric, we present Fast-DetectGPT, an optimized zero-shot detector, which substitutes DetectGPT's perturbation step with a more efficient sampling step. Our evaluations on various datasets, source models, and test conditions indicate that Fast-DetectGPT not only surpasses DetectGPT by a relative around 75% in both the white-box and black-box settings but also accelerates the detection process by a factor of 340, as detailed in Table 1. See \url{https://github.com/baoguangsheng/fast-detect-gpt} for code, data, and results.

• The paper presents Fast-DetectGPT, which employs conditional probability curvature to distinguish machine-generated from human-written text with a 75% accuracy improvement.
• It replaces DetectGPT's costly perturbation step with an efficient sampling approach, streamlining detection across both white-box and black-box settings.
• The method underscores the potential of token-level statistical analysis to enhance authenticity verification and combat misinformation and plagiarism.

Efficient Zero-Shot Detection of Machine-Generated Text

The paper "Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature" introduces Fast-DetectGPT, a method for the efficient detection of machine-generated text. The authors critically address the pressing challenge brought about by the increasing prevalence of LLMs such as ChatGPT and GPT-4. These models, while showcasing impressive capabilities in generating coherent and contextually relevant text, introduce challenges in distinguishing machine-generated content from human-written text, raising concerns about misinformation and plagiarism.

Core Contributions

Fast-DetectGPT builds upon the concept of conditional probability curvature to differentiate between machine-generated and human-authored content. This approach leverages the statistical tendency of LLMs to favor high-probability word choices, providing a robust zero-shot detection method that markedly improves upon its predecessor, DetectGPT. By replacing DetectGPT's computationally expensive perturbation step with a more efficient sampling step, Fast-DetectGPT offers significant improvements in both accuracy and speed.

Key findings from the paper indicate that Fast-DetectGPT achieves a detection accuracy improvement of approximately 75% over DetectGPT. Furthermore, the process is accelerated by a factor of 340x, underlying its potential in practical applications. The work covers evaluations on various datasets using different experimental setups, illustrating a consistent performance enhancement across both white-box (where the source model is known) and black-box (without source model knowledge) settings.

Numerical Results and Analysis

Fast-DetectGPT demonstrates an impressive AUROC performance of 0.9887 in detecting machine-generated texts in the white-box setting. A comparison of detection capabilities between the developed method and other zero-shot classifiers showcases Fast-DetectGPT's superiority. It further excels in the black-box setting, offering competitive results against current methods without explicit knowledge of the source model, which remains a notable achievement.

The authors also present an insightful analysis of Fast-DetectGPT's robustness across various domains and languages, supported by evaluations on datasets with distinct characteristics, such as XSum for news and WritingPrompts for stories. This highlights Fast-DetectGPT's versatility and adaptability in different contexts, a desirable attribute for real-world deployment.

Theoretical Implications and Future Directions

The introduction of conditional probability curvature as a feature offers a novel perspective in text detection, proposing a more granular approach by analyzing token-level probability metrics. This method represents a departure from traditional techniques focused on document-level metrics, providing a foundation for future exploration into more intricate attributes that differentiate human and machine text generation.

Anticipating future developments, the paper suggests directions for extending Fast-DetectGPT's capabilities. Notably, an exploration of optimal surrogate models for the black-box setting could further enhance its efficacy. Additional research may investigate the theoretical underpinnings of the method, offering a more detailed understanding of the conditional probability curvature metric.

Conclusion

By addressing the critical balance between computational efficiency and detection accuracy, Fast-DetectGPT emerges as a promising tool for discerning machine-generated text. The paper sets a significant milestone in the domain of AI text detection systems, ensuring the continued refinement of such technologies. As the landscape of LLMs evolves, methodologies like Fast-DetectGPT hold promise in safeguarding the authenticity and trustworthiness of information disseminated across digital platforms.