Summary of Model Merging and Safety Alignment: One Bad Model Spoils the Bunch, by Hasan Abed Al Kader Hammoud et al.
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
by Hasan Abed Al Kader Hammoud, Umberto Michieli, Fabio Pizzati, Philip Torr, Adel Bibi, Bernard Ghanem, Mete Ozay
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 explores the technique of merging large language models (LLMs) to create a single versatile model while retaining the expertise of the original ones. However, current approaches often overlook the importance of safety alignment during merging, leading to highly misaligned models. The authors evaluate several popular model merging techniques and demonstrate that existing methods not only transfer domain expertise but also propagate misalignment. To address this problem, they propose a two-step approach involving synthetic safety and domain-specific data generation, followed by incorporating these generated data into the optimization process of existing data-aware model merging techniques. The results show that integrating alignment-related data during merging can lead to models that excel in both domain expertise and alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about combining multiple language models together to create a better one. Right now, when we do this, we often get models that are good at one thing but not the other. The authors looked at how different ways of combining these models affect their ability to be safe and good at what they’re supposed to do. They found that most methods actually make things worse by spreading misalignment around. To fix this, they came up with a new way of combining the models that involves generating extra data that helps them become safer and better. |
Keywords
» Artificial intelligence » Alignment » Optimization
