What We are Missing in Multimodal LLM Evaluation?
Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu
Why It Matters
What makes this one worth your time
Understanding the limitations of current evaluation methods is crucial for advancing the development of multimodal AI systems and ensuring they meet real-world needs.
This paper highlights critical gaps in evaluating multimodal large language models.
Summary
The paper reviews current evaluation methods for multimodal large language models (MLLMs) and identifies gaps in existing benchmarks, emphasizing the need for improved metrics that assess integration across modalities.
Key contributions
- A comprehensive review of current evaluation methods for MLLMs.
- Identification of specific gaps in existing benchmark taxonomies.
- Recommendations for new evaluation criteria to better assess multimodal intelligence.
Notable insights
- The paper identifies specific gaps in evaluation metrics, such as temporal-spatial coherence and multimodal consistency, which are often overlooked.
- It suggests that existing benchmarks may not adequately measure a model's ability to integrate information across different modalities.
Possible limitations
- Not stated in the abstract.
Abstract
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 benchmark taxonomy to identify gaps, including temporal-spatial coherence, physical world understanding, multimodal consistency, and selective attention. Addressing these gaps is essential for measuring real progress in multimodal intelligence and exposing capability boundaries.