Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
arXiv:2603.09095v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematically diagnose this "modality gap" by evaluating seven MLLMs across seven benchmarks in five input modes, spanning both synthetically rendered text and realistic document images from arXiv PDFs to Wikipedia pages. We find that the gap is highly sensitive to rendering choices such as font and resolution, and that natural document images often exhibit much smaller gaps,
The proliferation of multimodal LLMs has exposed performance discrepancies between text and image inputs, necessitating an immediate focus on understanding and mitigating these gaps.
This research provides crucial insights for optimizing multimodal LLMs, ensuring their accurate and reliable performance when processing diverse visual text inputs, which is critical for real-world applications.
The understanding of the 'modality gap' is deepened, revealing sensitivity to rendering choices and indicating that natural document images can exhibit smaller gaps, guiding future model development and deployment.
- · Multimodal LLM developers
- · Document AI platforms
- · Digitalization initiatives
- · AI-powered document processing
- · Inefficient multimodal LLM deployments
- · Applications reliant on perfect text-from-image recognition
Improved accuracy and reliability of multimodal LLMs in tasks involving textual information presented visually.
Faster adoption and broader utility of multimodal AI in enterprise and consumer applications requiring document understanding.
Enhanced automation of workflows involving mixed-modality data, potentially accelerating digital transformation across various sectors.
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Read at arXiv cs.CL