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Text Quality-Based Pruning for Efficient Training of Language Models
May 6, 2024, 4:42 a.m. | Vasu Sharma, Karthik Padthe, Newsha Ardalani, Kushal Tirumala, Russell Howes, Hu Xu, Po-Yao Huang, Shang-Wen Li, Armen Aghajanyan, Gargi Ghosh
cs.LG updates on arXiv.org arxiv.org
Abstract: In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this paper we propose a novel method for numerically evaluating text quality in large unlabelled NLP datasets in a model agnostic manner to assign the text instances a "quality score".
By proposing the text quality metric, the paper establishes a framework to identify and eliminate low-quality text instances, leading to improved …
abstract arxiv cs.ai cs.cl cs.lg datasets language language models lms massive nlp novel paper process pruning quality text training type
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