Summary of Alternate Preference Optimization For Unlearning Factual Knowledge in Large Language Models, by Anmol Mekala et al.
Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models
by Anmol Mekala, Vineeth Dorna, Shreya Dubey, Abhishek Lalwani, David Koleczek, Mukund Rungta, Sadid Hasan, Elita Lobo
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The paper proposes a novel approach called Alternate Preference Optimization (AltPO) to efficiently eliminate the influence of specific training data, known as the forget set, from Large Language Models (LLMs). The existing unlearning methods rely solely on negative feedback, which often results in nonsensical or inconsistent outputs. AltPO combines negative feedback with in-domain positive feedback on the forget set. New evaluation metrics are introduced to assess the quality of responses related to the forget set. Experimental results show that AltPO enables effective unlearning while maintaining overall model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper wants to help machines “forget” some training data, but current methods don’t work well because they only give negative feedback. This can make the machine produce silly or confusing answers. The researchers came up with a new way called Alternate Preference Optimization (AltPO) that gives both positive and negative feedback. They also created new ways to measure how good this unlearning is. By doing this, AltPO helps machines forget training data without hurting their overall performance. |
Keywords
* Artificial intelligence * Optimization
