Summary of On Active Privacy Auditing in Supervised Fine-tuning For White-box Language Models, by Qian Sun et al.
On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models
by Qian Sun, Hanpeng Wu, Xi Sheryl Zhang
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a novel approach to identifying and quantifying privacy leakage risks during the fine-tuning of language models (LMs). The authors introduce a framework called Parsing, which leverages improved white-box membership inference attacks (MIAs) to monitor the privacy of LMs’ fine-tuning process. The framework uses novel learning objectives and a two-stage pipeline to detect potential privacy risks. The authors demonstrate the effectiveness of their approach on various models, including GPT-2, Llama2, and their variants. The paper aims to provide a reliable privacy auditing tool for the SFT community of LMs and offer insights into safeguarding privacy during fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about keeping language models private. Right now, there are big concerns that these models might reveal personal information because they’re trained on sensitive data. The authors created a new tool called Parsing to help identify and fix these privacy problems. They used special attacks to test the models and found out which ones were leaking personal info. This is important because it helps keep our personal data safe when we use language models like GPT-2 or Llama2. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Inference » Parsing
