Summary of Sok: Membership Inference Attacks on Llms Are Rushing Nowhere (and How to Fix It), by Matthieu Meeus et al.
SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)
by Matthieu Meeus, Igor Shilov, Shubham Jain, Manuel Faysse, Marek Rei, Yves-Alexandre de Montjoye
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper reviews the recent developments in Membership Inference Attacks (MIAs) against Large Language Models (LLMs). Specifically, it focuses on post-hoc evaluation setups, where sets of members and non-members are constructed after the release of a model. The authors show that these datasets suffer from strong distribution shifts, invalidating claims of LLMs memorizing strongly in real-world scenarios. They also introduce important considerations to properly evaluate MIAs against LLMs, including randomized test splits, injections of randomized sequences, and post-hoc control methods. The paper concludes by recommending approaches to benchmark sequence-level and document-level MIAs against LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well artificial intelligence models called Large Language Models (LLMs) can be tested to see if they are memorizing certain information. Researchers have been coming up with new ways to test these models, but most of these tests are done after the model is already released. This isn’t fair because it’s not like real life. The authors show that this way of testing is flawed and might not give us accurate results. They suggest some better ways to test these models in the future. |
Keywords
» Artificial intelligence » Inference
