What We are Missing in Multimodal LLM Evaluation?
While multimodal large language models (MLLMs) are advancing rapidly, current evaluation benchmarks fail to keep pace. This research highlights critical gaps in assessing how these models truly integrate cross-modal information.
Computer Science > Artificial Intelligence
Title: What We are Missing in Multimodal LLM Evaluation?
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.
Source: arXiv cs.AI Recent
