Summary of Safety-aware Fine-tuning Of Large Language Models, by Hyeong Kyu Choi et al.
Safety-Aware Fine-Tuning of Large Language Models
by Hyeong Kyu Choi, Xuefeng Du, Yixuan Li
First submitted to arxiv on: 13 Oct 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 The proposed Safety-Aware Fine-Tuning (SAFT) framework is designed to automatically detect and remove potentially harmful data from Large Language Models (LLMs), addressing concerns about the inclusion of harmful samples. By leveraging a scoring function that exploits subspace information, SAFT can reduce the presence of harmful content by up to 27.8%. The approach demonstrates efficacy across various LLMs and contamination rates, making it suitable for real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAFT is a new way to make sure Large Language Models don’t include bad words or ideas. Right now, people have to look at the data and decide what’s okay and what’s not. This can take a lot of time and might not be very fair. SAFT makes it easier by using special math to find the yucky stuff and remove it. It works pretty well, even with different kinds of language models and lots of bad words. This could be important for making sure the internet is a nice place. |
Keywords
» Artificial intelligence » Fine tuning
