  Our research focuses on the crucial challenge of discerning text produced by
Large Language Models (LLMs) from human-generated text, which holds
significance for various applications. With ongoing discussions about attaining
a model with such functionality, we present supporting evidence regarding the
feasibility of such models. We evaluated our models on multiple datasets,
including Twitter Sentiment, Football Commentary, Project Gutenberg, PubMedQA,
and SQuAD, confirming the efficacy of the enhanced detection approaches. These
datasets were sampled with intricate constraints encompassing every
possibility, laying the foundation for future research. We evaluate
GPT-3.5-Turbo against various detectors such as SVM, RoBERTa-base, and
RoBERTa-large. Based on the research findings, the results predominantly relied
on the sequence length of the sentence.
