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cloud.google.com·6h ago
AI Research
arXiv CS.AI·6d ago·5 min read
Strong Helps Weak: Directional Cross-Modal Alignment Transfer in Multi-modal LLMs
TL;DR
Directional Cross-modal Alignment Transfer (DCAT) enhances the performance of data-scarce target modalities in multi-modal large language models (MLLMs) by leveraging well-aligned, data-rich source modalities without additional fine-tuning.
✦ Why It Matters
Implement DCAT to improve your multi-modal models without incurring high training costs.
Key Takeaways
- Utilize DCAT to enhance performance in data-scarce modalities.
- Leverage strong source modalities for alignment transfer.
- Reduce training costs by avoiding extensive dataset requirements.
- Apply the closed-form weight-space solution for efficient calibration.
How It Works
DCAT transfers alignment from a well-aligned source modality to a weaker target modality by enhancing the relationship between modality-specific tokens and textual tokens. This is achieved through a mutual-information framework that quantifies alignment-related quantities, leading to improved performance on target benchmarks.