MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark

TL;DR

This paper introduces MME-Industry, a comprehensive, manually curated benchmark with 1050 questions across 21 industrial domains, designed to evaluate Multimodal Large Language Models' performance in real-world settings.

Contribution

It presents a novel, domain-specific evaluation benchmark for MLLMs, including multilingual data and complex tasks, to better assess industrial application capabilities.

Findings

Insights into MLLMs' industrial performance across domains

Identification of strengths and weaknesses in multilingual capabilities

Guidance for future model optimization in industrial contexts

Abstract

With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this paper, we introduce MME-Industry, a novel benchmark designed specifically for evaluating MLLMs in industrial settings.The benchmark encompasses 21 distinct domain, comprising 1050 question-answer pairs with 50 questions per domain. To ensure data integrity and prevent potential leakage from public datasets, all question-answer pairs were manually crafted and validated by domain experts. Besides, the benchmark's complexity is effectively enhanced by incorporating non-OCR questions that can be answered directly, along with tasks requiring specialized domain knowledge. Moreover, we provide both Chinese and English versions of the benchmark, enabling comparative analysis of MLLMs' capabilities across these languages. Our findings contribute valuable insights into MLLMs' practical industrial applications and illuminate promising directions for future model optimization research.