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About this paper
Zimeng Huang, Jinxin Ke, Xiaoxuan Fan, Yufeng Yang, Yang Liu, Liu Zhonghan, Zedi Wang, Junteng Dai and 4 more
MM-OPERA introduces an open-ended benchmark designed to evaluate association reasoning in large vision-language models (LVLMs), drawing inspiration from human psychometric tests. Through comprehensive evaluation, the benchmark reveals that current state-of-the-art LVLMs notably underperform humans in generating high-quality and diverse associative responses, particularly struggling with pattern abstraction and robust knowledge integration.
Key insights
- Associative intelligence requires deep cross-domain knowledge activation and flexible, non-conservative reasoning, an area where current LVLMs demonstrate considerable deficiencies.
- The ability to abstract and transfer associative patterns (In-Context Association) poses a greater challenge for most LVLMs than direct remote-item association, demanding more advanced meta-reasoning capabilities.
- While LVLMs can often achieve logical coherence (Reasonableness) in their reasoning steps, they typically lack the precise conceptualization (Distinctiveness) and comprehensive domain knowledge (Knowledgeability) required for truly profound associative insights.
Summary by alphaXiv.
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