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About this paper
Paul Pu Liang, Yiwei Lyu, Gunjan Chhablani, Nihal Jain, Zihao Deng, Xingbo Wang, Louis-Philippe Morency, Ruslan Salakhutdinov
Researchers from Carnegie Mellon University and collaborators introduce MULTIVIZ, a four-stage framework designed to visualize and understand the internal decision-making processes of complex multimodal models. The framework enhances human comprehension of model behavior, facilitates fine-grained error analysis, and contributes to debugging efforts, as demonstrated by up to a 30.5% accuracy improvement on specific tasks after bug identification.
Key insights
- A structured, multi-stage interpretability framework improves human users' ability to understand and accurately simulate complex multimodal model behaviors.
- Deep features within multimodal models can be linked to human-interpretable concepts through local and global representation analysis, enhancing model transparency.
- Errors in multimodal models can be precisely localized to specific processing stages (unimodal perception, cross-modal interaction, or final prediction logic) using comprehensive visualizations, facilitating targeted debugging.
Summary by alphaXiv.
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