A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish (2403.16139v1)

Abstract: LLMs are trained on massive web-crawled corpora. This poses risks of leakage, including personal information, copyrighted texts, and benchmark datasets. Such leakage leads to undermining human trust in AI due to potential unauthorized generation of content or overestimation of performance. We establish the following three criteria concerning the leakage issues: (1) leakage rate: the proportion of leaked data in training data, (2) output rate: the ease of generating leaked data, and (3) detection rate: the detection performance of leaked versus non-leaked data. Despite the leakage rate being the origin of data leakage issues, it is not understood how it affects the output rate and detection rate. In this paper, we conduct an experimental survey to elucidate the relationship between the leakage rate and both the output rate and detection rate for personal information, copyrighted texts, and benchmark data. Additionally, we propose a self-detection approach that uses few-shot learning in which LLMs detect whether instances are present or absent in their training data, in contrast to previous methods that do not employ explicit learning. To explore the ease of generating leaked information, we create a dataset of prompts designed to elicit personal information, copyrighted text, and benchmarks from LLMs. Our experiments reveal that LLMs produce leaked information in most cases despite less such data in their training set. This indicates even small amounts of leaked data can greatly affect outputs. Our self-detection method showed superior performance compared to existing detection methods.

Transparency in LLMs: An Experimental Survey on Data Leakage

Introduction to Data Leakage in LLMs

LLMs, due to their reliance on vast corpora sourced from the web, face significant risks regarding the leakage of sensitive information. This leakage encompasses a wide variety, including personal data, copyrighted material, and specific datasets crucial for benchmarking, thereby posing a challenge to the reliability of AI and its acceptance by the public. The paper presented in the paper under consideration explores this issue through a comprehensive experimental survey. It elucidates the dynamics between the proportion of leaked data (leakage rate) and its ease of generation (output rate) and detection (detection rate), thereby offering new insights into mitigating these risks.

Survey Scope and Methodology

The research introduced a unique approach to address leakage issues, proposing a self-detection method relying on few-shot learning, distinct from traditional methods not leveraging explicit learning. By constructing a dataset featuring prompts designed to evoke responses containing leaked information, the paper investigates the LLMs' propensity to generate such information. Surprisingly, it found that LLMs often produce leaked information, suggesting a significant impact even from minimal amounts of leaked data in their training sets. The self-detection method demonstrated superior detection performance compared to existing methods, emphasizing the need for enhanced mechanisms to safeguard against data leakage.

Leakage, Output, and Detection Rates

A critical contribution of the paper is the exploration of the relationship between the leakage, output, and detection rates. The survey revealed that despite the low leakage rate of certain types of data in pre-training datasets, the impact on the models' output tendencies remains pronounced. The findings suggest that a mere reduction in the leakage rate doesn't necessarily promise reduced risks of data leakage.

• Leakage Rate: The analysis of pre-training datasets from publicly available LLMs highlighted varying degrees of data leakage concerning personal information, copyrighted texts, and benchmark data.
• Output Rate: Despite the differences in leakage rates, the ease of generating leaked output did not significantly vary across different types of data, challenging the effectiveness of current data management practices in LLM pre-training.
• Detection Rate: The paper's self-detection method outperformed existing detection approaches, emphasizing the advantage of LLMs learning explicitly to differentiate between leaked and non-leaked data.

Theoretical and Practical Implications

The investigation draws attention to the urgent need for developing more sophisticated data management and detection tools tailored for LLM training environments. Theoretically, it challenges existing understandings of the leakage problem by demonstrating that the presence of leaked data, regardless of its proportion, significantly affects LLM outputs. Practically, the superior performance of the self-detection method advocates for a methodological shift towards approaches that enable LLMs to actively recognize and mitigate instances of data leakage, possibly paving the way for safer deployment of LLMs in sensitive applications.

Future Directions in AI and Data Leakage

The paper speculates on future advancements in AI to further mitigate the risks associated with data leakage. It underscores the potential of enhancing self-detection mechanisms, perhaps through more sophisticated few-shot learning techniques or integrating methodologies that mirror human reasoning processes more closely. The prospect of LLMs not just detecting but also rectifying instances of data leakage autonomously may significantly enhance the trustworthiness and safety of AI technologies.

Conclusion

This comprehensive survey on transparency and data leakage in LLMs not only highlights the complex relationship between the leakage rate and its impact on LLM outputs and detection but also introduces a novel self-detection approach that significantly outperforms existing methods. As LLMs continue to evolve and integrate into more aspects of our lives, addressing the challenges posed by data leakage with innovative and effective solutions will be paramount to ensuring their beneficial and secure application across various domains.