Summary of Order Of Magnitude Speedups For Llm Membership Inference, by Rongting Zhang and Martin Bertran and Aaron Roth
Order of Magnitude Speedups for LLM Membership Inference
by Rongting Zhang, Martin Bertran, Aaron Roth
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (stat.ML)
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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 Large Language Models (LLMs) have the potential to revolutionize computing, but their complexity and extensive training data also pose significant privacy vulnerabilities. One of the simplest risks associated with LLMs is membership inference attacks (MIAs), where an adversary tries to determine whether a specific data point was part of the model’s training set. Although this risk is well-known, current state-of-the-art methodologies for MIAs rely on computationally costly shadow models, making risk evaluation impractical for large models. This paper adapts a recent line of work using quantile regression to mount membership inference attacks and proposes a low-cost MIA that leverages an ensemble of small quantile regression models to determine if a document belongs to the model’s training set or not. The proposed approach is demonstrated on fine-tuned LLMs from various families (OPT, Pythia, Llama) and across multiple datasets, achieving comparable or improved accuracy compared to state-of-the-art shadow model approaches with as little as 6% of their computation budget. The paper also shows increased effectiveness against multi-epoch trained target models and architecture miss-specification robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure Large Language Models are safe from hackers who might try to figure out if a certain piece of data was used to train the model. Right now, there’s no easy way to do this for big models because it takes too much computer power. The researchers came up with a new way to do this that uses smaller versions of their method and can be done quickly. They tested it on different types of models and datasets and found that it worked just as well or even better than the old way, using only 6% of the same amount of computer power. This is important because it means we can make sure our language models are safe without needing too many powerful computers. |
Keywords
» Artificial intelligence » Inference » Llama » Regression
