Human Grounded Evaluation of Large Language Models for Optical Network Automation

Abstract:Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially across LLM families. We present HuGLEN, a stepwise evaluation pipeline that uses an LLM-as-a-judge together with a small set of expert ratings to enable scalable and reproducible comparison of candidate LLMs, and to rank them using a quality efficiency score (QES). We demonstrate HuGLEN for translating outputs from an explainable artificial intelligence (XAI) model for the optical network quality of transmission (QoT) estimation task into operator-friendly explanations. Our results show that a medium-sized LLM (12B parameters) achieves the highest QES, indicating the best trade-off between explanation quality and efficiency. Overall, HuGLEN reduces the human-labeling burden while supporting consistent model selection for operator-facing automation tasks.

Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)

arXiv:2607.18068 [cs.NI]

(or arXiv:2607.18068v1 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2607.18068 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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