Florenz: Scaling Laws for Systematic Generalization in Vision-Language
  Models
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).
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Julian Spravil
claimed_verified
2025-03-19T09:43:48.893Z
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Julian Spravil
user
Spravil
67da8b794e1138ddc328de0a
Sebastian Houben
admin_assigned
2025-03-19T13:47:14.517Z
654892f5c6dadd513f5a7143
/avatars/c167b45776391e02709246670e4843d1.svg
Sebastian Houben
user
EthylS2
67da8b794e1138ddc328de0b
Sven Behnke
admin_assigned
2025-03-19T13:47:23.428Z
65df1a033e759810359ad7c8
https://cdn-avatars.huggingface.co/v1/production/uploads/65df1a033e759810359ad7c8/51dnHqNxFuOT6pStpLYyX.jpeg
Sven Behnke
user
sven-behnke
Florenz: Scaling Laws for Systematic Generalization in Vision-Language
  Models
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).
Monolingual vision-language models achieve competitive performance across multilingual tasks through selective training and data generation, demonstrating scaling trends in translation and captioning.
cross-lingual transfer vision-language models multilingual language models monolingual encoder-decoder VLMs systematic generalization pre-trained VLM large language model synthetic dataset image captioning scaling law fine-tuning multimodal machine translation lexical disambiguation