Summary of Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging, by Jacob Morrison et al.
Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging
by Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi, Pang Wei Koh, Jesse Dodge, Pradeep Dasigi
First submitted to arxiv on: 16 Oct 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 This paper investigates the effectiveness of adapting general-purpose language models (LMs) to new skills by training on the new skills in isolation and then merging them with the original model. The authors propose a parallel-train-then-merge procedure, which is significantly cheaper than retraining the models on updated data mixtures. In experiments focusing on scientific literature understanding, safety, and coding, they find that this procedure is often comparably effective to retraining. Notably, parallel training is especially well-suited for enabling safety features in LMs, improving model compliance with safe prompts while preserving its ability to refuse dangerous or harmful prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us make language models (computer programs that can understand and generate human-like text) better at learning new skills. Right now, making these models learn new things is a slow and expensive process. The authors tested a new way of doing this by training the model on one new skill at a time and then combining it with its original abilities. They found that this method works just as well as retraining the entire model. This new approach can also help make sure language models are safe and responsible, refusing to generate harmful content. |
