The proposed Multi-turn Multimodal Interactive Dialogue (MMID) Benchmark enables comprehensive evaluation of the Perception, Memorization, and Reasoning abilities of MLLMs, and reveals MLLMs perform well with text-based input but degrade with images, requiring improved leverage fine-grained visual cues.
Seulgi Kim, Juoh Sun, Sumin Kim et al.· Proceedings of the 32nd ACM...· 0 citations
Flexible Self-Consistency (FSC) is proposed, which estimates problem difficulty as a continuous signal and dynamically adjusts the number of generated reasoning paths accordingly and leverages it as an indicator of model uncertainty to flexibly control the sampling budget.
Sihyeong Yeom, Geon Park, Geunyeong Jeong et al.· 0 citations
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in multimodal understanding and reasoning by integrating linguistic and visual information, while benchmarks facilitate iterative model improvement by evaluating their performance and analyzing their limitations. However, existing dialogue-based...
Seulgi Kim, Juoh Sun, Sumin Kim et al.· Proceedings of the 32nd ACM...· 0 citations
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