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Watermarking

# A Watermark for Large Language Models

John Kirchenbauer*, Jonas Geiping*, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein

* John Kirchenbauer and Jonas Geiping contributed equally.

First arXiv submission: January 24, 2023 · ICML 2023

Published version: July 2023. The publication and preprint records below retain their own dates and identifiers.

Embeds a statistical watermark by softly favoring a pseudorandom set of tokens during generation. It develops a detection test that does not require the language model and analyzes how the token distribution affects detection strength.

[Read paper ↗](https://proceedings.mlr.press/v202/kirchenbauer23a.html) · [PDF ↗](https://proceedings.mlr.press/v202/kirchenbauer23a/kirchenbauer23a.pdf) · [Code ↗](https://github.com/jwkirchenbauer/lm-watermarking)

## Relationship to Neural Linguistic Steganography

Its related-work section cites Neural Linguistic Steganography among earlier neural information-hiding methods. Its provenance-detection objective differs from concealing the existence of an arbitrary message.

## Scope and limitations

Low-entropy text offers less freedom to embed a detectable signal without affecting quality; detection and robustness depend on text length, entropy, and adversarial modifications.

## Cite this paper

### Published version

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein. [A Watermark for Large Language Models](https://proceedings.mlr.press/v202/kirchenbauer23a.html). Proceedings of the 40th International Conference on Machine Learning, 2023

[Published version BibTeX](https://steganography.live/reading-list/papers/llm-watermark/publication.bib) · [Published version CSL-JSON](https://steganography.live/reading-list/papers/llm-watermark/publication.csl.json)
Show BibTeX`@inproceedings{llm-watermark-publication,
  title = {{A Watermark for Large Language Models}},
  author = {Kirchenbauer, John and Geiping, Jonas and Wen, Yuxin and Katz, Jonathan and Miers, Ian and Goldstein, Tom},
  year = {2023},
  booktitle = {Proceedings of the 40th International Conference on Machine Learning},
  pages = {17061--17084},
  volume = {202},
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v202/kirchenbauer23a.html}
}
`

### arXiv preprint

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein. [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226v1). arXiv:2301.10226. First submission: January 24, 2023.

This citation records the first arXiv version and its original title. The version-specific link distinguishes it from later revisions; cite the version supporting your claim.

[arXiv preprint BibTeX](https://steganography.live/reading-list/papers/llm-watermark/preprint.bib) · [arXiv preprint CSL-JSON](https://steganography.live/reading-list/papers/llm-watermark/preprint.csl.json)
Show BibTeX`@misc{llm-watermark-preprint,
  title = {{A Watermark for Large Language Models}},
  author = {Kirchenbauer, John and Geiping, Jonas and Wen, Yuxin and Katz, Jonathan and Miers, Ian and Goldstein, Tom},
  year = {2023},
  publisher = {arXiv},
  eprint = {2301.10226v1},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2301.10226v1}
}
`

## Primary sources

- [Full authors, ICML venue, July 23–29 2023, PMLR volume and pages; no publication DOI listed.](https://proceedings.mlr.press/v202/kirchenbauer23a.html)

- [First submission January 24, 2023; later revised versions distinguished.](https://arxiv.org/abs/2301.10226)

- [Title-page equal contributions/code; sections 1.2–5 cover entropy and robustness; related work cites Ziegler et al. 2019.](https://proceedings.mlr.press/v202/kirchenbauer23a/kirchenbauer23a.pdf)

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