# MARQ infuses SE knowledge into LLMs for RE

Digest AI · Research · published 2026-10-28T04:00:00Z

Canonical: https://digestai.news/story/marq-infuses-se-knowledge-into-llms-for-re

## Summary

This study introduces MARQ, a modular prototype that supplements large language models (LLMs) with expert-derived systems engineering (SE) knowledge to improve requirement quality assessment. The team evaluated MARQ against off-the-shelf baselines on an expert-labeled NASA requirement set using Claude Opus 5, GPT-5.6 Sol, and Gemma 3 12B, each tested 100 times. Results showed that MARQ achieved median F1 scores of 0.80 for Claude Opus 5, 0.62 for GPT-5.6 Sol, and 0.53 for Gemma 3 12B, compared to off-the-shelf baselines of 0.55, 0.52, and 0.18 respectively.

## Key points

- MARQ infuses SE knowledge into LLMs
- Evaluations against off-the-shelf models conducted
- Gains in defect detection and false-alarm control observed

## Why it matters

These findings suggest that incorporating expert-derived SE knowledge can significantly improve the performance of LLM-based requirement quality assessment, potentially enhancing trustworthy AI support for systems engineering.

## Sources

1. [MARQ: A Modular, LLM-Agnostic Requirement Quality Assessor Infused with Systems Engineering Knowledge](https://arxiv.org/abs/2610.36234) (arXiv cs.AI, 2026-10-28, primary source)

## Cite

Digest AI, "MARQ infuses SE knowledge into LLMs for RE", 28 October 2026, https://digestai.news/story/marq-infuses-se-knowledge-into-llms-for-re

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