What We are Missing in Multimodal LLM Evaluation?
Computer Science > Artificial Intelligence arXiv:2606.26348 (cs) [Submitted on 24 Jun 2026] Title:What We are Missing in Multimodal LLM Evaluation? 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.
- ▪Computer Science > Artificial Intelligence arXiv:2606.26348 (cs) [Submitted on 24 Jun 2026] Title:What We are Missing in Multimodal LLM Evaluation?
- ▪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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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2606.26348 |
| Publication time | Fri, 26 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-26T05:20:40.950Z |
| Last seen | 2026-06-26T05:20:40.950Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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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