Resource-Efficient Language Models: Quantization for Fast and Accessible Inference

Large language models have significantly advanced natural language processing, yet their heavy resource demands pose severe challenges regarding hardware accessibility and energy consumption. This paper presents a focused and high-level review of post-training quantization (PTQ) techniques designed to optimize the inference efficiency of LLMs by the end-user, including details on various quantization schemes, granularities, and trade-offs. The aim is to provide a balanced overview between the theory and applications of post-training quantization.

Aligning the Norwegian UD Treebank with Entity and Coreference Information

This paper presents a merged collection of entity and coreference annotated data grounded in the Universal Dependencies (UD) treebanks for the two written forms of Norwegian: Bokm{\aa}l and Nynorsk. The aligned and converted corpora are the Norwegian Named Entities (NorNE) and Norwegian Anaphora Resolution Corpus (NARC). While NorNE is aligned with an older version of the treebank, NARC is misaligned and requires extensive transformation from the original annotations to the UD structure and CoNLL-U format. We here demonstrate the conversion and alignment processes, along with an analysis of discovered issues and errors in the data - some of which include data split overlaps in the original treebank. These procedures and the developed system may prove helpful for future corpus alignment and coreference annotation endeavors. The merged corpora comprise the first Norwegian UD treebank enriched with named entities and coreference information.