FinDocMRE: A Benchmark for Document-Level Financial Multimodal Reasoning Evaluation

TL;DR

FINDOCMRE is a comprehensive benchmark designed to evaluate large multimodal models' ability to perform document-level reasoning across financial reports, integrating text, images, and tables.

Contribution

The paper introduces a new multi-image, document-level benchmark for financial multimodal reasoning, constructed with a semi-automated pipeline ensuring high annotation quality.

Findings

No model exceeds 65% overall score on the benchmark.

Models perform well in semantic narrative but poorly in numerical estimation.

Significant performance gaps across different reasoning tasks.

Abstract

While Large Multimodal Models (LMMs) excel in general visual tasks, their deployment in specialized financial contexts remains insufficient. Existing benchmarks prioritize isolated charts, often overlooking the need to integrate data from text, tables, and images within comprehensive financial documents. To address this limitation, we introduce FINDOCMRE, a multi-image document-level benchmark designed for financial multimodal reasoning. We construct the dataset via a semi-automated pipeline that combines Visual-Centric Generation with Expert Verification, thereby minimizing text bias and ensuring high annotation quality. Spanning twelve domains, the benchmark comprises 12,207 samples derived from 2,878 financial reports, designed to evaluate multi-image processing and document-level understanding across five distinct task types. Extensive experiments with eleven representative LMMs reveal that no model surpasses an overall score of 65, highlighting challenges in integrating visual grounding with logical reasoning within complex document environments. Specifically, we observe a significant performance divergence across tasks, where models exhibit proficiency in semantic narrative construction but struggle with numerical estimation and cross-page visual grounding. FINDOCMRE serves as a rigorous benchmark to guide the evolution of financial LMMs towards expert-level document analysis and reasoning.