May 16, 2024, 4:46 a.m. | Muhammad Farid Adilazuarda

cs.CL updates on

arXiv:2311.12373v3 Announce Type: replace
Abstract: Significant progress has been made on text generation by pre-trained language models (PLMs), yet distinguishing between human and machine-generated text poses an escalating challenge. This paper offers an in-depth evaluation of three distinct methods used to address this task: traditional shallow learning, Language Model (LM) fine-tuning, and Multilingual Model fine-tuning. These approaches are rigorously tested on a wide range of machine-generated texts, providing a benchmark of their competence in distinguishing between human-authored and machine-authored linguistic …

abstract analysis arxiv beyond challenge comparative analysis evaluation generated human human and machine language language model language models machine paper progress replace text text generation turing type

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