Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression

• URL: http://arxiv.org/abs/2507.14997v1
• Date: Sun, 20 Jul 2025 15:05:24 GMT
• Title: Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression
• Authors: Roy H. Jennings, Genady Paikin, Roy Shaul, Evgeny Soloveichik,
• Abstract summary: We show how Transformer-Based Classification (RvTC) replaces vocabulary-constrained classification with a flexible bin-based approach.<n>Unlike generic task descriptions, prompts containing semantic information about specific images enable MLLMs to leverage cross-modal understanding.
• Score: 2.9998889086656586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) show promise for image-based regression tasks, but current approaches face key limitations. Recent methods fine-tune MLLMs using preset output vocabularies and generic task-level prompts (e.g., "How would you rate this image?"), assuming this mimics human rating behavior. Our analysis reveals these approaches provide no benefit over image-only training. Models using preset vocabularies and generic prompts perform equivalently to image-only models, failing to leverage semantic understanding from textual input. We propose Regression via Transformer-Based Classification (RvTC), which replaces vocabulary-constrained classification with a flexible bin-based approach. Unlike approaches that address discretization errors through complex distributional modeling, RvTC eliminates manual vocabulary crafting through straightforward bin increase, achieving state-of-the-art performance on four image assessment datasets using only images. More importantly, we demonstrate that data-specific prompts dramatically improve performance. Unlike generic task descriptions, prompts containing semantic information about specific images enable MLLMs to leverage cross-modal understanding. On the AVA dataset, adding challenge titles to prompts improves correlations from 0.83 to 0.90, a new state-of-the-art. We demonstrate through empirical evidence from the AVA and AGIQA-3k datasets that MLLMs benefit from semantic prompt information surpassing mere statistical biases. This underscores the importance of incorporating meaningful textual context in multimodal regression tasks.

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