Abstract
As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial in many applications. This tutorial aims to provide a comprehensive overview of text detection techniques, focusing on machine-generated text and deepfakes. We will discuss various methods for distinguishing between human-written and machine-generated text, including statistical methods, neural network-based techniques, and hybrid approaches. The tutorial will also cover the challenges in the detection process, such as dealing with evolving models and maintaining robustness against adversarial attacks. By the end of the session, attendees will have a solid understanding of current techniques and future directions in the field of text detection.- Anthology ID:
- 2024.acl-tutorials.6
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Luis Chiruzzo, Hung-yi Lee, Leonardo Ribeiro
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10–11
- Language:
- URL:
- https://aclanthology.org/2024.acl-tutorials.6
- DOI:
- Cite (ACL):
- Li Gao, Wenhan Xiong, and Taewoo Kim. 2024. Detecting Machine-Generated Text: Techniques and Challenges. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts), pages 10–11, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Machine-Generated Text: Techniques and Challenges (Gao et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-tutorials.6.pdf