Transferring Textual Preferences to Vision-Language Understanding through Model Merging

• URL: http://arxiv.org/abs/2502.13487v1
• Date: Wed, 19 Feb 2025 07:20:07 GMT
• Title: Transferring Textual Preferences to Vision-Language Understanding through Model Merging
• Authors: Chen-An Li, Tzu-Han Lin, Yun-Nung Chen, Hung-yi Lee,
• Abstract summary: This paper explores a training-free alternative by merging text-based reward models (RMs) with large vision-language models (LVLMs) Our approach shows that integrating these models leads to improved performance over LVLMs' scoring and text-based RMs.
• Score: 65.41765072566287
• License:
• Abstract: Large vision-language models (LVLMs) perform outstandingly across various multimodal tasks. However, their ability to evaluate generated content remains limited, and training vision-language reward models (VLRMs) with preference data is computationally expensive. This paper explores a training-free alternative by merging text-based reward models (RMs) with LVLMs to create VLRMs. Our approach shows that integrating these models leads to improved performance over LVLMs' scoring and text-based RMs, offering an efficient method for incorporating textual preferences into LVLMs.

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