Human Grounded Evaluation of Large Language Models for Optical Network Automation
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
Record details
Published: 20 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Jobs & economy · Transparency
Retrieved: 21 July 2026
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ethics.ai (20 July 2026), “Human Grounded Evaluation of Large Language Models for Optical Network Automation,” evidence record 12213, https://ethics.ai/record/12213 (originally published by arXiv cs.AI).
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