What We are Missing in Multimodal LLM Evaluation?
arXiv:2606.26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their capabilities have advanced rapidly, evaluation of such models has not kept pace. Most existing evaluation benchmarks are limited to isolated tasks and reveal little about whether a model integrates information across modalities. We examine current means for evaluating MLLMs and review the existing be
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Computer Science > Artificial Intelligence arXiv:2606.26348 (cs) [Submitted on 24 Jun 2026] Title:What We are Missing in Multimodal LLM Evaluation? Authors:Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu View a PDF of the paper titled What We are Missing in Multimodal LLM Evaluation?, by Po-han Li and 3 other authors View PDF HTML (experimental) Abstract:Multimodal large language models (MLLMs) can process diverse inputs, e.g., text, images, audio, and video, and generate textual responses. While their capabilities have advanced rapidly, evaluation of such models has not kept pace. Most existing evaluation benchmarks are limited to isolated tasks and reveal little about whether a model integrates information across modalities.
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