Summary of Privately Learning From Graphs with Applications in Fine-tuning Large Language Models, by Haoteng Yin et al.
Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
by Haoteng Yin, Rongzhe Wei, Eli Chien, Pan Li
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 This research paper proposes a privacy-preserving relational learning pipeline that decouples dependencies in sampled relations during training, ensuring differential privacy through a tailored application of DP-SGD. The method is applied to fine-tune large language models (LLMs) on sensitive graph data, tackling computational complexities. Evaluation is performed on LLMs of varying sizes (e.g., BERT, Llama2) using real-world relational data from four text-attributed graphs. Results demonstrate significant improvements in relational learning tasks while maintaining robust privacy guarantees during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps AI models work better with relationships and private information. It’s like a special way to make sure that personal data stays safe when training machines to understand connections between things. The researchers developed a new method that combines two existing techniques: one for keeping data private, and another for learning from relational data. They tested it on large language models using real-world data from four different sources. The results show that this approach can improve the accuracy of these models while still protecting sensitive information. |
Keywords
» Artificial intelligence » Bert
