Caption First, VQA Second: Knowledge Density, Not Task Format, Drives Multimodal Scaling

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Multimodal large language models (MLLMs) often exhibit less predictable scaling behavior than text-only LLMs, with increasing model size and task diversity yielding diminishing returns. This research argues that the primary bottleneck is not task format, but knowledge density in training data. Experiments demonstrate that Visual Question Answering (VQA) supervision contributes little incremental semantic information beyond image captions, as VQA signals can be reconstructed from captions with negligible performance loss. The study shows that increasing knowledge density through structured caption enrichment and cross-modal knowledge injection leads to consistent performance improvements across multimodal and downstream benchmarks. Performance correlates more strongly with semantic coverage than with task diversity, advocating for knowledge-centric multimodal training as a foundation for scalable MLLMs.

Key takeaway

For research scientists and MLLM developers focused on improving model scalability, prioritize increasing the semantic knowledge density of your training data over merely expanding task diversity. Your efforts should focus on constructing knowledge-rich multimodal corpora, potentially through semantically paired images and enriched captions, as this approach has shown consistent performance gains across various benchmarks, including business-specific tasks, without degrading core language capabilities.

Key insights

Knowledge density, not task format, is the primary driver for scaling multimodal large language models.

Principles

• Captions subsume most VQA-relevant semantic information.
• Semantic coverage dictates MLLM representational capacity.
• Task format shapes interaction, not knowledge expansion.

Method

A knowledge-centric data construction strategy increases semantic coverage by augmenting conventional image-caption supervision with comparative and relational knowledge derived from semantically related image pairs, using LLMs as both knowledge base and semantic filter.

In practice

• Replace VQA with enriched captions for MLLM training.
• Use LLMs to extract structured semantic descriptors for image pairing.
• Construct image pairs with coarse alignment and fine-grained contrast.

Topics

• Multimodal Large Language Models
• Knowledge Density
• Visual Question Answering
• Image Captioning
• Multimodal Scaling Laws

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.