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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation
TL;DR
Vision-Free Adaptation (VFA) is a new framework that enhances multilingual large language models (MLLMs) without relying on visual data. It effectively improves performance on multilingual tasks while maintaining multimodal capabilities using minimal text data.
✦ Why It Matters
Implement VFA to enhance your multilingual models with minimal data and maintain multimodal performance.
Key Takeaways
- Explore VFA to improve multilingual capabilities in your models.
- Utilize less than 2% of text data for effective training.
- Combine task vectors for enhanced model performance.
- Preserve multimodal capabilities while focusing on multilingual tasks.
How It Works
VFA decouples the enhancement of multilingual language capabilities from visual alignment by creating a multilingual task vector from a base LLM. This vector is then merged with the vision-aligned task vector of an MLLM, allowing for improved performance on multilingual tasks without compromising multimodal capabilities.