Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokens

TL;DR

This paper introduces an adaptive method for detecting pre-training data in large language models by identifying surprising tokens, which improves detection accuracy without needing access to training data or additional training.

Contribution

The proposed method adaptively locates surprising tokens based on model predictions, enhancing pre-training data detection without relying on training data access or reference models.

Findings

Achieves up to 29.5% improvement over existing methods.

Effective detection without access to pre-training data or additional training.

Introduces Dolma-Book benchmark for evaluation.

Abstract

While large language models (LLMs) are extensively used, there are raising concerns regarding privacy, security, and copyright due to their opaque training data, which brings the problem of detecting pre-training data on the table. Current solutions to this problem leverage techniques explored in machine learning privacy such as Membership Inference Attacks (MIAs), which heavily depend on LLMs' capability of verbatim memorization. However, this reliance presents challenges, especially given the vast amount of training data and the restricted number of effective training epochs. In this paper, we propose an adaptive pre-training data detection method which alleviates this reliance and effectively amplify the identification. Our method adaptively locates \textit{surprising tokens} of the input. A token is surprising to a LLM if the prediction on the token is "certain but wrong", which refers to low Shannon entropy of the probability distribution and low probability of the ground truth token at the same time. By using the prediction probability of surprising tokens to measure \textit{surprising}, the detection method is achieved based on the simple hypothesis that seeing seen data is less surprising for the model compared with seeing unseen data. The method can be applied without any access to the the pre-training data corpus or additional training like reference models. Our approach exhibits a consistent enhancement compared to existing methods in diverse experiments conducted on various benchmarks and models, achieving a maximum improvement of 29.5\%. We also introduce a new benchmark Dolma-Book developed upon a novel framework, which employs book data collected both before and after model training to provide further evaluation.