Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models
arXiv:2605.08145v2 Announce Type: replace-cross Abstract: Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues can be addressed by exploiting the shared information between modalities to compensate for the impaired one. To this end, we analyze multimodal interactions -- redundant (shared), unique (exclusive), and synergistic (emergent) task-relevant information provided by the modalities -- to determine their impacts on model reliability. Specifically, amplifying redundant interactions would increase
The proliferation of advanced vision-language models necessitates continuous improvements in robustness and hallucination mitigation, especially as these models are deployed in more critical applications.
Improving the robustness and reducing hallucinations in multimodal AI systems is crucial for their reliable integration into various industries and for building trustworthiness in autonomous agents.
This research outlines a method to enhance the reliability of vision-language models by strategically exploiting inherent redundancies, potentially leading to more stable and dependable AI applications.
- · AI developers
- · Generative AI platforms
- · Autonomous systems
- · Platforms with high hallucination rates
- · AI models lacking robustness features
Vision-language models become more reliable in interpreting ambiguous or corrupted inputs.
Reduced incidence of failures and improved safety in AI-driven applications, fostering greater adoption.
Accelerated development and deployment of truly autonomous AI agents capable of operating effectively in uncertain real-world environments.
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Read at arXiv cs.LG