MME-Industry: A Cross-Industry Multimodal Evaluation Benchmark
This addresses the need for better evaluation tools for MLLMs in industrial applications, though it is incremental as it builds on existing benchmark efforts.
The paper tackles the lack of comprehensive evaluation benchmarks for Multimodal Large Language Models (MLLMs) in industrial settings by introducing MME-Industry, a novel benchmark covering 21 domains with 1050 question-answer pairs, and provides insights into MLLMs' practical applications.
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.