Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights
📰 ArXiv cs.AI
Researchers propose a method to improve multilingual models by adding target language weights, reducing the need for extensive pre-training and high-quality instruction data
Action Steps
- Identify a pre-trained multilingual model as a base model
- Add target language weights to the base model to adapt it to a specific low-resource language
- Fine-tune the model on a small amount of target language data to improve performance
- Evaluate the model's performance on the target language task
Who Needs to Know This
NLP engineers and researchers working on multilingual models can benefit from this approach, as it provides a lightweight alternative to existing adaptation methods
Key Insight
💡 Adding target language weights can be a lightweight and effective way to adapt multilingual models to low-resource languages
Share This
💡 Improve multilingual models with target language weights! 🌎
Key Takeaways
Researchers propose a method to improve multilingual models by adding target language weights, reducing the need for extensive pre-training and high-quality instruction data
Full Article
Title: Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights
Abstract:
arXiv:2603.28263v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain heavily centered on English, with limited performance in low-resource languages. Existing adaptation approaches, such as continual pre-training, demand significant computational resources. In the case of instructed models, high-quality instruction data is also required, both of which are often inaccessible for low-resource language communities. Under these constraints, model merging offers a lightweight alterna
Abstract:
arXiv:2603.28263v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain heavily centered on English, with limited performance in low-resource languages. Existing adaptation approaches, such as continual pre-training, demand significant computational resources. In the case of instructed models, high-quality instruction data is also required, both of which are often inaccessible for low-resource language communities. Under these constraints, model merging offers a lightweight alterna
DeepCamp AI