Summary of Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?, by Michael-andrei Panaitescu-liess et al.
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
by Michael-Andrei Panaitescu-Liess, Zora Che, Bang An, Yuancheng Xu, Pankayaraj Pathmanathan, Souradip Chakraborty, Sicheng Zhu, Tom Goldstein, Furong Huang
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the effectiveness of watermarking Large Language Models (LLMs) as a deterrent against generating copyrighted texts. The authors first theoretically analyze and empirically evaluate the impact of watermarks on copyright infringement. They find that incorporating watermarks into LLMs significantly reduces the likelihood of generating copyrighted content, addressing concerns in deploying LLMs. However, they also discover that watermarking can have unintended consequences on Membership Inference Attacks (MIAs), which aim to detect whether a sample was part of the pretraining dataset and may be used to detect copyright violations. Surprisingly, watermarking is found to adversely affect the success rate of MIAs, complicating detecting copyrighted text in the pretraining dataset. The authors propose an adaptive technique to improve the success rate of a recent MIA under watermarking. Their findings highlight the importance of developing adaptive methods to study critical problems in LLMs with potential legal implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to stop Large Language Models (LLMs) from accidentally copying copyrighted texts. They try different ways to add “watermarks” to these models, which makes it less likely for them to generate copyrighted content. But they also find that this method can have an unexpected effect on a special kind of test called Membership Inference Attacks (MIAs). These tests help identify whether a sample was part of the training data or not. The authors discover that watermarking actually makes these tests harder, which is important because it could affect how we detect copyrighted text in the future. They also suggest a new way to improve the success rate of this test when using watermarks. |
Keywords
» Artificial intelligence » Inference » Likelihood » Pretraining
