Detecting Leaked Training Data in Large Language Models through Sampling-based Pseudo-Likelihood

Core Concepts

A sampling-based pseudo-likelihood method (SaMIA) can effectively detect whether a given text is included in the training data of a large language model, without requiring access to the model's likelihood or loss.

Abstract

The paper proposes a Sampling-based Pseudo-Likelihood (SPL) method for Membership Inference Attacks (MIA), called SaMIA, that can be applied to any large language model (LLM) without requiring access to the model's likelihood or loss. Key highlights: Large Language Models (LLMs) are trained on large-scale web data, which poses the risk of leaking inappropriate data such as benchmarks, personal information, and copyrighted texts in the training data. Membership Inference Attacks (MIA) aim to determine whether a given text is included in the model's training data. Existing MIA methods rely on the likelihood or loss computed by the model, which is not available for some proprietary LLMs like ChatGPT. SaMIA treats the target text as the reference text and generates multiple output texts from the LLM as candidate texts. It calculates the degree of n-gram match between the candidate texts and the reference text as the SPL, and determines the membership of the text in the training data based on this. Experiments on four publicly available LLMs (GPT-J-6B, OPT-6.7B, Pythia-6.9B, LLaMA-2-7B) show that SaMIA can achieve performance on par with existing likelihood-based methods. The paper also introduces a leakage detection method that combines SPL and information content, which achieves the highest average score among all existing methods. Analyses show that SaMIA performs best when using unigrams, and its performance improves with increasing number of text samples and length of the target text.

Stats

The training data of the LLMs used in the experiments (OPT-6.7B, Pythia-6.9B, LLaMA-2-7B, GPT-J-6B) include a variety of web data sources such as the Pile, CommonCrawl, Wikipedia, and PushShift.io Reddit. The WikiMIA benchmark dataset contains Wikipedia event pages, where texts from pages created before 2017 are considered leaked data (included in the training data of LLMs) and texts from pages created after 2023 are considered unleaked data.

Quotes

"Large Language Models (LLMs) bring about a game-changing transformation in various services used on a daily basis (Brown et al., 2020; Touvron et al., 2023). The pre-training of LLMs relies on massive-scale web data of mixed quality (Zhao et al., 2023)." "Membership Inference Attacks (MIA) consider the task of determining whether a given target text is included in the training data of a model (Shokri et al., 2016). Generally, because models are trained to fit the data, a text included in the training data tends to exhibit a higher likelihood compared to ones unseen in the training data (Yeom et al., 2017)."

Key Insights Distilled From

Sampling-based Pseudo-Likelihood for Membership Inference Attacks

by Masahiro Kan... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11262.pdf

Deeper Inquiries

How can the proposed SaMIA method be extended to detect leakage of other types of data beyond text, such as images or structured data, in the training of large multimodal models

The SaMIA method can be extended to detect leakage of other types of data beyond text by adapting the sampling-based pseudo-likelihood approach to incorporate features specific to images or structured data. For images, the method could involve generating multiple image samples based on a given prompt or partial image and then calculating a similarity metric between the generated images and the reference image. This similarity metric could be based on visual features like pixel values, color distributions, or structural elements. For structured data, the approach could involve generating variations of the structured data based on a partial input and then comparing the generated data with the reference structured data using relevant similarity measures specific to the data format, such as schema matching or attribute value comparisons. By customizing the sampling and similarity calculation process for different data types, the SaMIA method can be adapted to detect leakage in the training of large multimodal models effectively.

What are the potential limitations or failure cases of the SaMIA approach, and how can it be further improved to handle more challenging scenarios

While the SaMIA approach shows promising results in detecting text leakage in large language models, there are potential limitations and failure cases to consider. One limitation is the reliance on the ROUGE-N metric for measuring similarity, which may not capture all aspects of semantic or contextual similarity between texts. This could lead to false positives or false negatives in leakage detection, especially in cases where the generated text deviates significantly from the reference text while still being related. To address this, incorporating more advanced similarity metrics that consider semantic meaning or context could enhance the detection accuracy of SaMIA. Additionally, SaMIA may face challenges in handling highly diverse or noisy training data, where the generated samples may not accurately represent the full range of training data. Improvements in sampling strategies, such as incorporating diversity-promoting techniques or data augmentation, could help mitigate this issue and enhance the robustness of the method in challenging scenarios.

Given the growing concerns around the privacy and security implications of large language models, what other proactive measures can be taken by model developers and researchers to ensure the responsible development and deployment of these powerful AI systems

In addition to the SaMIA method, there are several proactive measures that model developers and researchers can take to ensure the responsible development and deployment of large language models. One key measure is to implement robust data governance practices, including thorough data auditing, documentation, and transparency regarding the sources and usage of training data. This can help mitigate the risk of unintentional data leakage and ensure compliance with privacy regulations. Furthermore, researchers can prioritize ethical considerations in model design by incorporating fairness, accountability, and transparency principles into the development process. This includes conducting bias assessments, fairness evaluations, and impact analyses to identify and address potential ethical issues in the model. Collaboration with multidisciplinary teams, including ethicists, legal experts, and domain specialists, can provide diverse perspectives and insights to guide responsible AI development. Additionally, continuous monitoring, auditing, and validation of model behavior post-deployment are essential to detect and address any emerging ethical or security concerns. By adopting a holistic approach that combines technical expertise with ethical considerations, model developers can promote the responsible and ethical use of large language models in society.