Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability
📰 ArXiv cs.AI
Learn to measure cross-modal synergy in Vision-Language Models (VLMs) and evaluate their explainability using a new benchmark
Action Steps
- Identify the limitations of current post-hoc explainers for VLMs
- Develop a benchmark to measure cross-modal synergy in VLMs
- Evaluate VLMs using the benchmark and unimodal perturbation metrics
- Analyze the results to understand the cross-modal reasoning of VLMs
- Apply the insights to improve the design and training of VLMs
Who Needs to Know This
Researchers and developers working on VLMs can benefit from this benchmark to improve the explainability of their models, while data scientists and engineers can use it to evaluate the performance of VLMs in various applications
Key Insight
💡 Current post-hoc explainers for VLMs have limitations due to cross-modal redundancy, and a new benchmark is needed to evaluate their explainability
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🚀 New benchmark for measuring cross-modal synergy in Vision-Language Models (VLMs) 🤖💡
Key Takeaways
Learn to measure cross-modal synergy in Vision-Language Models (VLMs) and evaluate their explainability using a new benchmark
Full Article
Title: Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability
Abstract:
arXiv:2605.22168v1 Announce Type: new Abstract: Vision-Language Models (VLMs) map complex visual inputs to semantic spaces, but interpreting the cross-modal reasoning of VLMs currently relies on post-hoc explainers evaluated via unimodal perturbation metrics. We expose a limitation in this paradigm: because multimodal datasets contain language priors and modality biases, VLMs frequently exhibit cross-modal redundancy, allowing them to answer visual queries using text alone. Consequently, unimoda
Abstract:
arXiv:2605.22168v1 Announce Type: new Abstract: Vision-Language Models (VLMs) map complex visual inputs to semantic spaces, but interpreting the cross-modal reasoning of VLMs currently relies on post-hoc explainers evaluated via unimodal perturbation metrics. We expose a limitation in this paradigm: because multimodal datasets contain language priors and modality biases, VLMs frequently exhibit cross-modal redundancy, allowing them to answer visual queries using text alone. Consequently, unimoda
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