Summary of Model-based Privacy-preserving Knowledge Transfer For Large Language Models, by Zhaomin Wu et al.
Model-Based Privacy-Preserving Knowledge Transfer for Large Language Models
by Zhaomin Wu, Jizhou Guo, Junyi Hou, Bingsheng He, Lixin Fan, Qiang Yang
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed framework, Llamdex, aims to enhance large language models (LLMs) by integrating domain-specific knowledge while ensuring privacy. Existing methods struggle to balance utility and privacy, with RAG compromising sensitive data and differentially private data synthesis techniques resulting in poor utility. Llamdex addresses this challenge by training models on domain-specific data and connecting them to LLMs through carefully designed modules. This approach improves the accuracy of domain-specific tasks by up to 26% compared to state-of-the-art methods under the same differential privacy constraints, while maintaining comparable inference efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Llamdex is a new way to help large language models learn more about specific topics, like medical research or financial news, without compromising people’s privacy. Right now, there are two main problems: one method lets LLMs access this information but risks sharing private data, while another method guarantees privacy but doesn’t work well. The researchers came up with a new approach that combines the strengths of both methods to get better results. |
Keywords
» Artificial intelligence » Inference » Rag
