Summary of Watermarking Language Models Through Language Models, by Xin Zhong et al.
Watermarking Language Models through Language Models
by Xin Zhong, Agnibh Dasgupta, Abdullah Tanvir
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 proposes a novel approach to watermarking language models through prompts generated by language models themselves. The framework involves three interconnected models: Prompting, Marking, and Detecting. The Prompting model generates instructions for embedding watermarks, while the Marking model embeds these watermarks into generated content. A Detecting model then verifies the presence of these watermarks. Experiments were conducted using ChatGPT and Mistral as the Prompting and Marking models, with detection accuracy evaluated using a pretrained classifier model. The results demonstrate high classification accuracy across various configurations, with 95% accuracy for ChatGPT and 88.79% for Mistral. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to mark language models so they can’t be copied or used without permission. They did this by creating three special models that work together. The first model gives instructions on how to add the watermark, the second model adds it to the generated text, and the third model checks if the watermark is there. They tested this system using two well-known language models, ChatGPT and Mistral, and found that they were able to correctly identify the watermarks most of the time. This could be useful for protecting content and making sure people know who created it. |
Keywords
» Artificial intelligence » Classification » Embedding » Prompting
