Summary of Reversing the Forget-retain Objectives: An Efficient Llm Unlearning Framework From Logit Difference, by Jiabao Ji et al.
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
by Jiabao Ji, Yujian Liu, Yang Zhang, Gaowen Liu, Ramana Rao Kompella, Sijia Liu, Shiyu Chang
First submitted to arxiv on: 12 Jun 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 Unlearning from Logit Difference (ULD) framework addresses the challenges of degenerated output and catastrophic forgetting in large language model unlearning tasks. The ULD method introduces an assistant LLM that aims to remember forget documents and forget retain knowledge, which is then used to derive the unlearned LLM by computing logit differences between the target and assistant LLMs. This approach resolves both challenges and improves training efficiency, achieving significant forgetting while preserving overall capabilities. Extensive experiments demonstrate improved model utility on benchmarks, with a notable 0% loss of model utility on the ToFU benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to “unlearn” information from large language models (LLMs) so they forget certain things without losing their ability to learn other things. This is important because LLMs can contain private or copyrighted information, and we need ways to make them forget this information safely. The new method, called Unlearning from Logit Difference (ULD), uses a special “assistant” model that tries to remember what the main model should forget and forget what it should remember. This helps the main model forget the right things without losing its overall abilities. The authors tested their method and found it worked well, preserving most of the original model’s capabilities while allowing it to safely “unlearn” certain information. |
Keywords
» Artificial intelligence » Large language model
