Florenz: Scaling Laws for Systematic Generalization in Vision-Language
  Models
Authors: name: Julian Spravil; name: Sebastian Houben; name: Sven Behnke
Published: 2025-03-12 14:41:10

Abstract: Cross-lingual transfer enables vision-language models (VLMs) to perform
vision tasks in various languages with training data only in one language.
Current approaches rely on large pre-trained multilingual language models.
However, they face the curse of multilinguality, sacrificing downstream task
performance for multilingual capabilities, struggling with lexical ambiguities,
and falling behind recent advances. In this work, we study the scaling laws of
systematic generalization with monolingual VLMs for multilingual tasks,
focusing on the impact of model size and seen training samples. We propose
Florenz, a monolingual encoder-decoder VLM with 0.4B to 11.2B parameters
combining the pre-trained VLM Florence-2 and the large language model Gemma-2.
Florenz is trained with varying compute budgets on a synthetic dataset that
features intentionally incomplete language coverage for image captioning, thus,
testing generalization from the fully covered translation task. We show that
not only does indirectly learning unseen task-language pairs adhere to a
scaling law, but also that with our data generation pipeline and the proposed
Florenz model family, image captioning abilities can emerge in a specific
language even when only data for the translation task is available. Fine-tuning
on a mix of downstream datasets yields competitive performance and demonstrates
promising scaling trends in multimodal machine translation (Multi30K, CoMMuTE),
lexical disambiguation (CoMMuTE), and image captioning (Multi30K, XM3600, COCO
Karpathy).