MMTabReal: Real-World Benchmark for Multimodal Table Understanding

ACL ARR 2026 January Submission9856 Authors

Keywords: multimodal table dataset, data, creation, benchmarking, table QA, multimodal QA, vision question answering, NLP datasets

Abstract: Multimodal tables i.e. tabular layouts interleaved with charts, maps, icons, and color encodings are ubiquitous in real applications yet remain difficult for Multimodal Large Language Models (MLLMs). Despite advances in text and image understanding, systematic evaluation of table-centric multimodal reasoning is limited. We introduce MMTabReal, a MultiModal Table Benchmark, human-curated suite of 500 real-world tables paired with 4021 question–answer pairs. MMtabReal spans four question types, five reasoning categories, and eight structural archetypes. Evaluations of state-of-the-art models reveal substantial gaps, especially in visual grounding, spatial alignment, and multi-step inference, with 20–40% performance drops relative to existing benchmarks. These results highlight the need for architectures that more tightly fuse vision with tabular structure and support explicit numeric/logical operations. MMtabReal is released for evaluation only, providing a rigorous, reproducible testbed that reflects the linguistic, structural, and reasoning complexity of real-world multimodal tables.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: benchmarking, table QA, multimodal QA, vision question answering, NLP datasets

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 9856