Summary of Can Llms Get Help From Other Llms Without Revealing Private Information?, by Florian Hartmann et al.
Can LLMs get help from other LLMs without revealing private information?
by Florian Hartmann, Duc-Hieu Tran, Peter Kairouz, Victor Cărbune, Blaise Aguera y Arcas
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
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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 This paper investigates the application of cascade systems in machine learning, specifically focusing on large language models (LLMs). Cascade systems allow local models to query remote LLMs when needed, but raise concerns about privacy risks when sensitive data is involved. To mitigate this issue, the authors propose techniques for equipping local models with privacy-preserving methods, reducing the risk of information leakage. Two new privacy measures are introduced to quantify this leakage. The proposed system leverages social learning, where LLMs collaborate and learn from each other. Results on several datasets show that the approach minimizes privacy loss while improving task performance compared to a non-cascade baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning models in a way that protects people’s private information. When we use large language models (LLMs) to help smaller models make decisions, there’s a risk that sensitive data could be sent to the remote model and accessed without permission. To fix this problem, researchers developed ways to make the local models more secure and less likely to leak personal info. They came up with new methods to measure how much privacy is lost when using these systems. By having LLMs work together and learn from each other, they can improve performance while keeping private data safe. |
Keywords
* Artificial intelligence * Machine learning
