AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
On a large-scale all-words WSD task, the encoder model not only outperformed the decoder model but also generated substantially lower carbon emissions – an eight-fold reduction.
The results show that encoder-based metrics remain highly competitive, while generative LLMs perform strongly in hypothesis comparison and improve the interpretability of ASR evaluation.
Alation studies reveal a strong synergy between POS features and CRF decoding, with the combined approach yielding the best single-model performance, and ensembling models trained with different objectives improves both overall F1 score and discontinuous MWE scores, demonstrating the importance of training diversity fo...
LLM-assisted sense assignment with a Serbian WordNet-based custom inventory, iterative inventory expansion, and expert validation is combined with a constrained JSON-formatted output to support the practical construction and refinement of sense-annotated resources in a low-resource setting.
A data-centric analysis of semantic knowledge acquisition in word embeddings, focusing on word analogy and semantic similarity shows that, for relational semantics, training-data quality outweighs quantity, and that simple proxy models remain a practical, interpretable tool for efficient data selection.
Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeasured axis is referential reach, the distance a segment must look back into its document to resolve the entities and pronouns it contains. We formalize this as discourse...
Results show that BPE produces the most compact and morphologically aligned subword representations, while the modified Unigram LM achieved the best overall downstream performance across tasks, underscore that tokenizer choice and vocabulary design are critical determinants of language model efficiency and performance...
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