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New framework evaluates LLMs' ability to generate Design Structure Matrices

By PulseAugur Editorial · [3 sources] ·

This paper introduces a black-box evaluation framework designed to assess how well Large Language Models (LLMs) can generate Design Structure Matrices (DSMs) from technical documentation. The framework uses a reproducible methodology to compare LLM-generated DSMs against manually validated ground-truth matrices. It incorporates structural, classification, and stability metrics, culminating in a Composite Quality Score (Q). Experiments reveal that while LLMs can produce plausible DSMs with good reproducibility on well-structured inputs, they struggle with ambiguity, inconsistent definitions, and prompt variations, highlighting current limitations in LLM-driven automation for model-based systems engineering. AI

IMPACT This framework could accelerate the integration of LLMs into model-based systems engineering by providing a standardized way to audit their performance in generating complex technical matrices.

RANK_REASON The cluster contains an academic paper detailing a new evaluation framework for LLM capabilities.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New framework evaluates LLMs' ability to generate Design Structure Matrices

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Niels Potters, Theo Hofman ·

    Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation

    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-…

  2. arXiv cs.AI TIER_1 English(EN) · Theo Hofman ·

    Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation

    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…

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