May 14, 2024, 4:49 a.m. | Jisu Kim, Juhwan Lee

cs.CL updates on arXiv.org arxiv.org

arXiv:2405.07490v1 Announce Type: new
Abstract: The rapid advancement of Large Language Models (LLMs) has improved text understanding and generation but poses challenges in computational resources. This study proposes a curriculum learning-inspired, data-centric training strategy that begins with simpler tasks and progresses to more complex ones, using criteria such as prompt length, attention scores, and loss values to structure the training data. Experiments with Mistral-7B (Jiang et al., 2023) and Gemma-7B (Team et al., 2024) models demonstrate that curriculum learning slightly …

abstract advancement arxiv challenges computational cs.ai cs.cl curriculum curriculum learning data data-centric language language model language models large language large language model large language models llms ones performance resources strategy study tasks text text understanding through training type understanding

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