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Research questionHow can network operators detect risky LLM-generated configurations before deployment, even when translation accuracy is high?A configuration can accurately express a high-level intent yet still contain deployment-relevant ambiguity or risk. Translation-accuracy scores alone do not reveal which generated configurations warrant caution or where the ambiguity originates.
AI
Evaluation & Benchmarks
Machine Learning
Natural Language Processing
Technology
Latest papersRecent research connected to this question, newest first.Uncertainty Signals for Network Intent Translation: Risk Ranking and Ambiguity LocalizationThe evidence concerns an LLM fine-tuned for intent translation to a vendor-specific Juniper EX3300 switch platform. It evaluates sampling-based predictive uncertainty and token-level entropy on an ambiguity-controlled test set across context types and sampling budgets; predictive uncertainty ranked risk but was substantially miscalibrated with less informative contexts, while token entropy correlated with parameter- and description-sourced ambiguity.research paper · Sep 3, 2026
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