March 26, 2024, 4:43 a.m. | Hansi Hettiarachchi, Damith Premasiri, Lasitha Uyangodage, Tharindu Ranasinghe

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.16571v1 Announce Type: cross
Abstract: The introduction of large language models (LLMs) has advanced natural language processing (NLP), but their effectiveness is largely dependent on pre-training resources. This is especially evident in low-resource languages, such as Sinhala, which face two primary challenges: the lack of substantial training data and limited benchmarking datasets. In response, this study introduces NSINA, a comprehensive news corpus of over 500,000 articles from popular Sinhala news websites, along with three NLP tasks: news media identification, news …

abstract advanced arxiv benchmarking challenges cs.ai cs.cl cs.lg data datasets face introduction language language models language processing languages large language large language models llms low natural natural language natural language processing nlp pre-training processing resources training training data type

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