Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models
This addresses concerns about detecting copyrighted material usage in LLM training, though it is incremental as it builds on prior work to overcome limitations in experimental setups.
The paper tackles the problem of membership inference attacks (MIA) on large language models (LLMs), showing that MIA can succeed when testing with multiple documents rather than single sentences, and achieves the first successful MIA on pre-trained and fine-tuned LLMs using an adapted baseline method.
Membership inference attacks (MIA) attempt to verify the membership of a given data sample in the training set for a model. MIA has become relevant in recent years, following the rapid development of large language models (LLM). Many are concerned about the usage of copyrighted materials for training them and call for methods for detecting such usage. However, recent research has largely concluded that current MIA methods do not work on LLMs. Even when they seem to work, it is usually because of the ill-designed experimental setup where other shortcut features enable "cheating." In this work, we argue that MIA still works on LLMs, but only when multiple documents are presented for testing. We construct new benchmarks that measure the MIA performances at a continuous scale of data samples, from sentences (n-grams) to a collection of documents (multiple chunks of tokens). To validate the efficacy of current MIA approaches at greater scales, we adapt a recent work on Dataset Inference (DI) for the task of binary membership detection that aggregates paragraph-level MIA features to enable MIA at document and collection of documents level. This baseline achieves the first successful MIA on pre-trained and fine-tuned LLMs.