Summary of If You Can’t Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs, by Muhammad Khalifa et al.
If You Can’t Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs
by Muhammad Khalifa, Yi-Chern Tan, Arash Ahmadian, Tom Hosking, Honglak Lee, Lu Wang, Ahmet Üstün, Tom Sherborne, Matthias Gallé
First submitted to arxiv on: 5 Dec 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 paper explores the idea of merging large language models by combining their strengths and weaknesses. Specifically, it looks at recycling model checkpoints from different training runs, which often show tradeoffs between various language capabilities such as instruction following and code generation. The authors develop an optimization algorithm that tunes the weights of each checkpoint to create a Pareto-optimal model that outperforms individual models and baselines. They find that good merges tend to include almost all checkpoints with non-zero weights, suggesting that even seemingly bad initial checkpoints can contribute to a good final merge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about combining big language models in a way that makes them better. Imagine you have lots of models trained on different things, like following instructions or generating code. Each model has its own strengths and weaknesses. The authors are trying to find the best way to combine these models so they can work together really well. They’re using an optimization algorithm to figure out how much to use each model in a new model that’s even better than the originals. |
Keywords
» Artificial intelligence » Optimization
