AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A unified evaluation of LLMs on Arabic morphosyntactic tagging and dependency parsing, covering pre-tokenized, raw-text, and cascaded settings, and compares zero-shot prompting with retrieval-based in-context learning.
This work introduces SemCog Bench, a curated benchmark of 1,858 Arabic--Hebrew word pairs with sentence-level annotations for cognate identification and semantic disambiguation and finds that context and scale yield model-dependent gains, while original-script inputs generally perform best.
A genre-diverse sentence segmentation corpus spanning eight genres and a wide range of punctuation and document structure conditions is introduced, using AraSEG to evaluate LLMs, lightweight encoder models, and dependency parser-based models under increasingly challenging segmentation settings.
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