5 papers · 1 filter
Do Modern Video-LLMs Need to Listen? A Benchmark Audit and Scalable Remedy
Geewook Kim, Minjoon Seo
Speech and audio encoders developed over years of community effort are routinely excluded from video understanding pipelines, not because they fail, but because benchmarks never re…
MMRefine: Unveiling the Obstacles to Robust Refinement in Multimodal Large Language Models
Gio Paik, Geewook Kim, Jinbae Im
This paper introduces MMRefine, a MultiModal Refinement benchmark designed to evaluate the error refinement capabilities of Multimodal Large Language Models (MLLMs). As the emphasi…
Instruction Tuning with and without Context: Behavioral Shifts and Downstream Impact
Hyunji Lee, Seunghyun Yoon, Yunjae Won +7
Instruction tuning is a widely used approach to improve the instruction-following ability of large language models (LLMs). Instruction-tuning datasets typically include a mixture o…
State-Space Hierarchical Compression with Gated Attention and Learnable Sampling for Hour-Long Video Understanding in Large Multimodal Models
Geewook Kim, Minjoon Seo
We propose an efficient framework to compress massive video-frame features before feeding them into large multimodal models, thereby mitigating the severe token explosion arising f…
Evaluating Multimodal Generative AI with Korean Educational Standards
Sanghee Park, Geewook Kim
This paper presents the Korean National Educational Test Benchmark (KoNET), a new benchmark designed to evaluate Multimodal Generative AI Systems using Korean national educational…