MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space

ACL ARR 2025 May Submission817 Authors

Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in interpreting visual layouts and text. However, a significant challenge remains in their ability to interpret robustly and reason over multi-tabular data presented as images, a common occurrence in real-world scenarios like web pages and digital documents. Existing benchmarks typically address single tables or non-visual data (text/structured). This leaves a critical gap: they don't assess the ability to parse diverse table images, correlate information across them, and perform multi-hop reasoning on the combined visual data. To bridge this evaluation gap, we introduce MTabVQA, a novel benchmark specifically designed for multi-tabular visual question answering. MTabVQA comprises 3,745 complex question-answer pairs that necessitate multi-hop reasoning across several visually rendered table images. We provide extensive benchmark results for state-of-the-art VLMs on MTabVQA, revealing significant performance limitations. We further investigate post-training techniques to enhance these reasoning abilities and release MTabVQA-Instruct, a large-scale instruction-tuning dataset. Our experiments show that fine-tuning VLMs with MTabVQA-Instruct substantially improves their performance on visual multi-tabular reasoning. Code and dataset are available online: \href{https://anonymous.4open.science/r/MTabVQA-EMNLP-B16E}.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: corpus creation, benchmarking, table QA, multimodality, NLP datasets, multimodal QA

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 817