Feb. 2, 2024, 3:41 p.m. | Xinlin Peng Ying Zhou Ben He Le Sun Yingfei Sun

cs.CL updates on arXiv.org arxiv.org

Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks. However, the utilization of these models carries inherent risks, including but not limited to plagiarism, the dissemination of fake news, and issues in educational exercises. Although several detectors have been proposed to address these concerns, their effectiveness against adversarial perturbations, specifically in the context of student essay writing, remains largely unexplored. This paper aims to bridge this gap by constructing AIG-ASAP, an AI-generated student essay dataset, employing a …

adversarial capabilities concerns cs.ai cs.cl detection educational essay evaluation fake fake news generated language language models large language large language models llms plagiarism risks tasks text text generation

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