Summary of Mod: a Distribution-based Approach For Merging Large Language Models, by Quy-anh Dang et al.
MoD: A Distribution-Based Approach for Merging Large Language Models
by Quy-Anh Dang, Chris Ngo
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Mixture of Distributions (MoD) framework merges large language models by operating directly on their output probability distributions, allowing for efficient knowledge sharing across tasks while preserving individual model capabilities. This novel approach outperforms traditional weight-averaging methods and existing model merging techniques on multiple mathematical reasoning benchmarks using Qwen2.5 models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be merged to share knowledge and improve performance. A new way of combining these models, called Mixture of Distributions (MoD), is better than other methods at solving math problems. This works well because it looks at the model’s guesses, not just its weight or importance. |
Keywords
* Artificial intelligence * Probability
