arXiv:2607.05985v1 Announce Type: new Abstract: This paper presents a black-box evaluation framework to systematically assess the ability of Large Language Models (LLMs) to generate Design Structure Matrices (DSMs) from structured technical documentation. Motivated by the closed-source nature of current Auto-DSM pipelines, the framework introduces a reproducible methodology that benchmarks generated DSMs (GEN-DSMs) against manually validated ground-truth matrices (GT-DSMs). The evaluation integrates both single-run and multi-run perspectives, combining structural metrics (Completeness, Correct
The proliferation of Large Language Models necessitates robust evaluation frameworks to ensure their reliable application in complex engineering tasks like Design Structure Matrix generation.
This framework addresses a critical need for transparent and reproducible assessment of LLM-generated outputs, especially in domains requiring high accuracy and reliability, bypassing the black-box nature of current systems.
The ability to systematically evaluate and benchmark LLMs for specific engineering tasks like DSM generation will accelerate their integration into industrial design and planning processes.
- · AI developers
- · Engineering firms
- · LLM researchers
- · Manual design processes
- · Inefficient software tools
Improved reliability and trust in LLM-generated engineering artifacts.
Faster design cycles and reduced human error in complex system architecture development.
LLMs become indispensable tools for early-stage engineering design, profoundly changing workflows.
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Read at arXiv cs.AI