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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs
TL;DR
A new framework, the Capability-Driven Multimodal Scaling Law, predicts vision-language model (VLM) performance based on observable textual capabilities, improving backbone selection. This method enhances decision-making by quantifying transfer rates and absorption efficiency across model families.
✦ Why It Matters
Use the Capability-Driven Multimodal Scaling Law to select LLM backbones more effectively for your VLM projects.
Key Takeaways
- Utilize the new scaling law for informed backbone selection.
- Evaluate textual benchmarks to avoid negative impacts on VLM performance.
- Prefer base LLMs over instruction-tuned models for better results.
- Leverage the framework to streamline VLM training predictions.
How It Works
The Capability-Driven Multimodal Scaling Law uses a low-dimensional capability score, derived from principal component analysis (PCA) of LLM benchmarks, to model VLM performance. It incorporates a per-backbone transfer rate and an absorption rate, which quantifies how efficiently data scales with model size.
This allows for accurate predictions of VLM performance based on observable textual capabilities.