Summary of Unlocking the Potential Of Model Merging For Low-resource Languages, by Mingxu Tao et al.
Unlocking the Potential of Model Merging for Low-Resource Languages
by Mingxu Tao, Chen Zhang, Quzhe Huang, Tianyao Ma, Songfang Huang, Dongyan Zhao, Yansong Feng
First submitted to arxiv on: 4 Jul 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 This research proposes an alternative approach to adapting large language models (LLMs) to new languages, particularly those with limited data. The traditional method involves continual pre-training (CT) followed by supervised fine-tuning (SFT), but this approach struggles to balance language modeling and task-solving capabilities in low-resource languages. Instead, the authors suggest model merging, which combines models with distinct capabilities into a single model without additional training. This approach is demonstrated using Llama-2-7B, showing that it effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data. The study also analyzes the merging process and introduces a slack variable to the model merging algorithm to enhance performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work better for languages that don’t have much data. Usually, we train these models on lots of text, but it’s hard to do this for languages with limited data. The authors came up with an idea called “model merging” which combines different models into one without needing more training data. They tested this idea using a big language model and found that it worked really well. This means we can make language models work better for languages with little data, which is important because many people around the world speak these languages. |
Keywords
* Artificial intelligence * Fine tuning * Language model * Llama * Supervised
