$ $The usage of generative artificial intelligence (AI) tools based on large
language models, including ChatGPT, Bard, and Claude, for text generation has
many exciting applications with the potential for phenomenal productivity
gains. One issue is authorship attribution when using AI tools. This is
especially important in an academic setting where the inappropriate use of
generative AI tools may hinder student learning or stifle research by creating
a large amount of automatically generated derivative work. Existing plagiarism
detection systems can trace the source of submitted text but are not yet
equipped with methods to accurately detect AI-generated text. This paper
introduces the idea of direct origin detection and evaluates whether generative
AI systems can recognize their output and distinguish it from human-written
texts. We argue why current transformer-based models may be able to self-detect
their own generated text and perform a small empirical study using zero-shot
learning to investigate if that is the case. Results reveal varying
capabilities of AI systems to identify their generated text. Google's Bard
model exhibits the largest capability of self-detection with an accuracy of
94\%, followed by OpenAI's ChatGPT with 83\%. On the other hand, Anthropic's
Claude model seems to be not able to self-detect.

本文介绍了直接来源检测的概念，并评估了生成型 AI 系统是否能够识别其输出并将其与人工编写的文本加以区分。结果表明，Google 的 Bard 模型表现出最大的自检测能力，准确率达到 94％，其次是 OpenAI 的 ChatGPT，准确率为 83％。而 Anthropic 的 Claude 模型似乎无法自检测。