模仿游戏：在ChatGPT和BARD时代检测人类与AI生成的文本
The Imitation Game: Detecting Human and AI-Generated Texts in the Era of ChatGPT and BARD
AI总结：
本研究引入一个包含散文、故事、诗歌和Python代码的人类与LLM生成文本数据集，采用多种机器学习模型进行分类，发现模型在二元分类中表现优越，但区分GPT生成文本更具挑战性，为AI文本检测提供了启示。
摘要：
基于人工智能的大型语言模型（LLMs）在革新教育、研究和实践方面具有相当大的潜力。然而，区分人类撰写的文本和AI生成的文本已成为一项重要任务。本文提出了一项比较研究，引入了一个新颖的数据集，包含不同体裁的人类撰写和LLM生成的文本：散文、故事、诗歌和Python代码。我们采用多种机器学习模型对文本进行分类。结果表明，尽管数据集的样本量有限，这些模型在区分人类和AI生成的文本方面表现出有效性。然而，当对GPT生成的文本进行分类时，任务变得更加具有挑战性，尤其是在故事写作方面。结果表明，在二元分类任务中，例如区分人类生成的文本与特定LLM生成的文本，模型表现出优越的性能，相比之下，涉及区分人类生成和多个LLM生成的更复杂的多类任务则更具挑战性。我们的发现为AI文本检测提供了富有洞察力的启示，同时我们的数据集为这一不断发展的领域的未来研究铺平了道路。
英文摘要：
The potential of artificial intelligence (AI)-based large language models (LLMs) holds considerable promise in revolutionizing education, research, and practice. However, distinguishing between human-written and AI-generated text has become a significant task. This paper presents a comparative study, introducing a novel dataset of human-written and LLM-generated texts in different genres: essays, stories, poetry, and Python code. We employ several machine learning models to classify the texts. Results demonstrate the efficacy of these models in discerning between human and AI-generated text, despite the dataset's limited sample size. However, the task becomes more challenging when classifying GPT-generated text, particularly in story writing. The results indicate that the models exhibit superior performance in binary classification tasks, such as distinguishing human-generated text from a specific LLM, compared to the more complex multiclass tasks that involve discerning among human-generated and multiple LLMs. Our findings provide insightful implications for AI text detection while our dataset paves the way for future research in this evolving area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
