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Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?
Created by
Haebom
Author
Yang Yao, Lingyu Li, Jiaxin Song, Chiyu Chen, Zhenqi He, Yixu Wang, Xin Wang, Tianle Gu, Jie Li, Yan Teng, Yingchun Wang
Outline
This paper addresses the limitations of multimodal large-scale language models (MLLMs) in their ability to perceive visual details and make common-sense causal inferences. We present Argus Inspection, a multimodal benchmark with two challenging levels that integrates detailed visual perception and real-world common-sense understanding to assess causal inference capabilities. Furthermore, we present the Eye of Panoptes framework, which integrates a binary parameter sigmoid metric and indicator functions to enable a more comprehensive evaluation of MLLM responses in opinion-based reasoning tasks. Experimental results on 26 leading MLLMs show that the best performance in visual detail-aware inference is only 0.46, indicating significant room for improvement.
Takeaways, Limitations
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Takeaways:
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We present a new benchmark (Argus Inspection) and evaluation framework (Eye of Panoptes) for assessing MLLM's visual detail recognition and common-sense causal inference abilities.
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Presenting the current level of visual detail recognition ability of MLLM and the need for improvement.
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Proposing a more comprehensive evaluation method for opinion-based reasoning tasks.
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Limitations:
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Further review of the difficulty setting and generalizability of the Argus Inspection benchmark is needed.
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Further research is needed on the optimization of sigmoid metrics and indicator functions in the Eye of Panoptes framework.
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Further research is needed to ensure diversity in the MLLMs being evaluated.
