AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, standard benchmarks for machine translation are approaching saturation. Further, automatic translation metrics are unreliable, opaque, and vulne...
The results emphasize the need for calibrating the LLM judges with a sample with reference-aware evaluation before using them in reference-free setups reliably, and the methodology provides a blueprint for researchers and practitioners in doing such calibration of LLM judges for other tasks.
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