Summary of Open Llms Are Necessary For Current Private Adaptations and Outperform Their Closed Alternatives, by Vincent Hanke et al.
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
by Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola Emmanuel Olatunji, Michael Backes, Adam Dziedzic
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 abstract discusses the limitations of open Large Language Models (LLMs) in matching the performance of proprietary counterparts. Researchers have proposed new methods to adapt closed LLMs to private data without compromising privacy. This work analyzes the privacy protection and performance of four recent methods for private adaptation, evaluating their threat models, differential privacy (DP), various architectures, datasets, classification, and generation tasks. The findings indicate that all methods leak query data to the LLM provider, three methods leak private training data, one method requires a local open LLM, and all exhibit lower performance compared to private gradient-based adaptation methods for local open LLMs. Furthermore, the costs of training and querying are higher than using alternative methods on local open LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to adapt closed Large Language Models (LLMs) to work with private data without sharing it with anyone. Researchers have tried different approaches, but this study shows that all these methods still share some private information. The results show that even the best method still leaks sensitive user data and also requires a lot of training and money. Overall, the paper suggests using open LLMs instead for better privacy and performance. |
Keywords
* Artificial intelligence * Classification
