Year: 2026
Type: conference-paper
Abstract: Large vision-language models (LVLMs) have made significant progress in chart understanding.However, financial charts, characterized by complex temporal structures and domainspecific terminology, remain notably underexplored.We introduce FinChart-Bench, the first benchmark specifically focused on realworld financial charts.FinChart-Bench comprises 1,200 financial chart images collected from 2015 to 2024, each annotated with True/-False (TF), Multiple Choice (MC), and Question Answering (QA) questions, totaling 7,016 questions.We conducted a comprehensive evaluation of 26 state-of-the-art LVLMs on FinChart-Bench.Our evaluation reveals critical insights: (1) the performance gap between open-source and closed-source models is narrowing, (2) performance degradation occurs in upgraded models within families, (3) many models struggle with instruction following, (4) both advanced models show significant limitations in spatial reasoning abilities, and (5) current LVLMs are not reliable enough to serve as automated evaluators.These findings highlight important limitations in current LVLM capabilities for financial chart understanding.The FinChart-Bench dataset is available at https: //github.com/Tizzzzy/FinChart-Bench.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4417432474