AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Evaluating multiple open-weight LLM families across scales, it is found that decreasing lexical frequency produces a consistent, monotonic decrease in sentence likelihood, but only a modest reduction in overall contrastive acceptability accuracy.
Camellia is introduced, a benchmark for evaluating entity-centric cultural biases in nine Asian languages, spanning six Asian cultures, and it is found that LLMs can struggle with context understanding in some Asian languages, creating performance gaps between cultures in entity extraction.
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