UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents

• URL: http://arxiv.org/abs/2602.07038v1
• Date: Tue, 03 Feb 2026 12:04:56 GMT
• Title: UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents
• Authors: Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun,
• Abstract summary: Recent Large Multimodal Models have shown promising potential for performing end-to-end KIE directly from document images.<n>We introduce UNIKIE-BENCH, a benchmark designed to rigorously evaluate the KIE capabilities of LMMs.<n>Experiments on 15 state-of-the-art LMMs reveal substantial performance degradation under diverse schema definitions, long-tail key fields, and complex layouts.
• Score: 65.14244917622881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Key Information Extraction (KIE) from real-world documents remains challenging due to substantial variations in layout structures, visual quality, and task-specific information requirements. Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images. To enable a comprehensive and systematic evaluation across realistic and diverse application scenarios, we introduce UNIKIE-BENCH, a unified benchmark designed to rigorously evaluate the KIE capabilities of LMMs. UNIKIE-BENCH consists of two complementary tracks: a constrained-category KIE track with scenario-predefined schemas that reflect practical application needs, and an open-category KIE track that extracts any key information that is explicitly present in the document. Experiments on 15 state-of-the-art LMMs reveal substantial performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios. These findings underscore persistent challenges in grounding accuracy and layout-aware reasoning for LMM-based KIE. All codes and datasets are available at https://github.com/NEUIR/UNIKIE-BENCH.

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