DesignQA: A Multimodal Benchmark for Evaluating Large Language Models’ Understanding of Engineering Documentation
Name
jcise_25_2_021009.pdf
Description
Published version
Size
1.03 MB
Format
Adobe PDF
Checksum (MD5)
e0edca27774315414b61dd9b8bd96846
Author(s)
Doris, Anna C
Grandi, Daniele
Tomich, Ryan
Alam, Md Ferdous
Ataei, Mohammadmehdi
Cheong, Hyunmin
Ahmed, Faez
Date Issued
December 23, 2024
Journal
Journal of Computing and Information Science in Engineering
Publisher
ASME International
Citation
Doris, A. C., Grandi, D., Tomich, R., Alam, M. F., Ataei, M., Cheong, H., and Ahmed, F. (December 23, 2024). "DesignQA: A Multimodal Benchmark for Evaluating Large Language Models’ Understanding of Engineering Documentation." ASME. J. Comput. Inf. Sci. Eng. February 2025; 25(2): 021009.
Version
Final published version
Abstract
This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requirements in technical documentation. Developed with a focus on real-world engineering challenges, DesignQA uniquely combines multimodal data—including textual design requirements, CAD images, and engineering drawings—derived from the Formula SAE student competition. Unlike many existing MLLM benchmarks, DesignQA contains document-grounded visual questions where the input image and the input document come from different sources. The benchmark features automatic evaluation metrics and is divided into segments—Rule Comprehension, Rule Compliance, and Rule Extraction—based on tasks that engineers perform when designing according to requirements. We evaluate state-of-the-art models (at the time of writing) like GPT-4o, GPT-4, Claude-Opus, Gemini-1.0, and LLaVA-1.5 against the benchmark, and our study uncovers the existing gaps in MLLMs’ abilities to interpret complex engineering documentation. The MLLMs tested, while promising, struggle to reliably retrieve relevant rules from the Formula SAE documentation, face challenges in recognizing technical components in CAD images and encounter difficulty in analyzing engineering drawings. These findings underscore the need for multimodal models that can better handle the multifaceted questions characteristic of design according to technical documentation. This benchmark sets a foundation for future advancements in AI-supported engineering design processes. DesignQA is publicly available at online.
Terms of Use
All rights reserved
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4067333
