Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGPT as a data labeler or annotator. We evaluate the zero-shot performance of ChatGPT in the task of human-written vs. AI-generated text detection, and perform experiments on publicly available datasets. We empirically investigate if ChatGPT is symmetrically effective in detecting AI-generated or human-written text. Our findings provide insight on how ChatGPT and similar LLMs may be leveraged in automated detection pipelines by simply focusing on solving a specific aspect of the problem and deriving the rest from that solution. All code and data is available at https://github.com/AmritaBh/ChatGPT-as-Detector.
Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?
ChatGPT's zero-shot performance is evaluated for detecting AI-generated versus human-written text using publicly available datasets.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 2
- Hosting
- Abstract onlyARXIV-DEFAULT
Cite
Notes
Only stored in your browser.
Attribution
- Abstract & full text
- arxiv.org/abs/2308.01284v2ARXIV-DEFAULT
- TL;DR
- Semantic Scholar