Summary of Unified Parameter-efficient Unlearning For Llms, by Chenlu Ding et al.
Unified Parameter-Efficient Unlearning for LLMs
by Chenlu Ding, Jiancan Wu, Yancheng Yuan, Jinda Lu, Kai Zhang, Alex Su, Xiang Wang, Xiangnan He
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 introduces a novel instance-wise unlearning framework, LLMEraser, which addresses privacy and security concerns when fine-tuning Large Language Models (LLMs) for specific domains using Parameter-Efficient Fine-Tuning (PEFT) strategies like LoRA. The framework systematically categorizes unlearning tasks and applies precise parameter adjustments using influence functions, allowing it to efficiently manage various unlearning scenarios without compromising model performance. The authors demonstrate the effectiveness of LLMEraser through extensive experiments on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that Large Language Models don’t accidentally keep or share sensitive information when they’re fine-tuned for specific tasks. Right now, people are using a technique called LoRA to make these models work better for certain jobs, but this can be a problem if it means the model keeps learning things it shouldn’t know. To solve this issue, the researchers created a new way to “unlearn” or remove unwanted knowledge from the model. This method is called LLMEraser and it’s designed to be efficient and effective at handling different types of unlearning tasks without making the model worse. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Parameter efficient
