@inproceedings{zhao-etal-2024-watermarking,
title = "Watermarking for Large Language Models",
author = "Zhao, Xuandong and
Wang, Yu-Xiang and
Li, Lei",
editor = "Chiruzzo, Luis and
Lee, Hung-yi and
Ribeiro, Leonardo",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.luhme-tutorials.6/",
doi = "10.18653/v1/2024.acl-tutorials.6",
pages = "10--11",
abstract = "As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial in both the computational linguistics and machine learning communities. In this tutorial, we aim to provide an in-depth exploration of text watermarking, a subfield of linguistic steganography with the goal of embedding a hidden message (the watermark) within a text passage. We will introduce the fundamentals of text watermarking, discuss the main challenges in identifying AI-generated text, and delve into the current watermarking methods, assessing their strengths and weaknesses. Moreover, we will explore other possible applications of text watermarking and discuss future directions for this field. Each section will be supplemented with examples and key takeaways."
}
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<abstract>As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial in both the computational linguistics and machine learning communities. In this tutorial, we aim to provide an in-depth exploration of text watermarking, a subfield of linguistic steganography with the goal of embedding a hidden message (the watermark) within a text passage. We will introduce the fundamentals of text watermarking, discuss the main challenges in identifying AI-generated text, and delve into the current watermarking methods, assessing their strengths and weaknesses. Moreover, we will explore other possible applications of text watermarking and discuss future directions for this field. Each section will be supplemented with examples and key takeaways.</abstract>
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%0 Conference Proceedings
%T Watermarking for Large Language Models
%A Zhao, Xuandong
%A Wang, Yu-Xiang
%A Li, Lei
%Y Chiruzzo, Luis
%Y Lee, Hung-yi
%Y Ribeiro, Leonardo
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhao-etal-2024-watermarking
%X As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial in both the computational linguistics and machine learning communities. In this tutorial, we aim to provide an in-depth exploration of text watermarking, a subfield of linguistic steganography with the goal of embedding a hidden message (the watermark) within a text passage. We will introduce the fundamentals of text watermarking, discuss the main challenges in identifying AI-generated text, and delve into the current watermarking methods, assessing their strengths and weaknesses. Moreover, we will explore other possible applications of text watermarking and discuss future directions for this field. Each section will be supplemented with examples and key takeaways.
%R 10.18653/v1/2024.acl-tutorials.6
%U https://aclanthology.org/2024.luhme-tutorials.6/
%U https://doi.org/10.18653/v1/2024.acl-tutorials.6
%P 10-11
Markdown (Informal)
[Watermarking for Large Language Models](https://aclanthology.org/2024.luhme-tutorials.6/) (Zhao et al., ACL 2024)
ACL
- Xuandong Zhao, Yu-Xiang Wang, and Lei Li. 2024. Watermarking for Large Language Models. 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.