Benchmarking Large Vision-Language Models on CFMME: A Comprehensive Chinese Financial Multimodal Evaluation Dataset

Researchers introduce CFMME, a Chinese financial multimodal evaluation benchmark containing 6,052 instances to assess Large Vision-Language Models' capabilities in financial contexts. Testing shows current state-of-the-art LVLMs achieve 66.11% accuracy on financial question-answering tasks, indicating significant room for improvement in applying these models to real-world financial applications.

This research addresses a critical gap in AI evaluation frameworks by creating the first comprehensive benchmark specifically designed to test Large Vision-Language Models within Chinese financial domain contexts. While LVLMs have demonstrated impressive capabilities across general tasks, their performance on specialized financial applications remains largely unmeasured, creating uncertainty for institutions considering deployment in banking, trading, and financial analysis workflows.

The CFMME benchmark's structure reflects real financial business processes, incorporating eight distinct image modalities and four core multimodal tasks spanning from academic knowledge to complex real-world scenarios. This design philosophy ensures evaluation relevance rather than abstract performance metrics. The 66.11% accuracy achieved by leading models on financial question-answering reveals substantial gaps—a concerning result given that financial institutions typically demand higher reliability standards before adopting new technologies.

For the fintech and banking sectors, these findings signal both opportunity and caution. The performance ceiling indicates genuine development potential as researchers optimize LVLMs specifically for financial applications, potentially creating valuable competitive advantages for early adopters. However, the current capability gaps underscore that blindly deploying general-purpose LVLMs into financial workflows introduces unacceptable risk. The detailed error analysis and cross-modal capability insights provided by this benchmark should guide development priorities for future model improvements.

Looking forward, this benchmark establishes evaluation standards that will likely influence how financial institutions assess AI technologies. As researchers use CFMME to iterate on specialized financial LVLMs, we should expect gradual accuracy improvements that eventually make these models viable for specific high-value financial tasks, though generalized financial AI deployment remains several iterations away.

• →State-of-the-art LVLMs achieve only 66.11% accuracy on Chinese financial question-answering tasks, revealing significant capability gaps.
• →CFMME benchmark covers 6,052 financial instances across eight image modalities, establishing rigorous evaluation standards for the financial AI domain.
• →Current models show substantial room for improvement in perception, reasoning, and cognition tasks within financial workflows.
• →Specialized financial LVLMs derived from this research could create competitive advantages for fintech and banking institutions.
• →The benchmark provides detailed error analysis to guide future development of domain-specific vision-language models.