AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work curates a human-annotated topic-utterance judgments dataset and examines the superior performance of lightweight LLM matchers over embedding and regex models when equipped with natural language descriptions.
This work tests whether a language's typological distance from the others predicts its ideal number of co-training languages, and finds no dependable rule: any apparent relationship rests on a couple of languages and does not hold up.
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