avatarDariusz Gross #DATAsculptor

Summary

Researchers propose a watermarking framework for large language models to mitigate potential harm caused by their outputs.

Abstract

The article discusses the potential risks associated with large language models (LLMs) like ChatGPT, such as social engineering, election manipulation, and the creation of fake news. To address these concerns, a group of researchers has proposed a watermarking framework that embeds signals into generated text, making it detectable without knowledge of the model parameters or access to the language model API. The framework has been tested using a multi-billion parameter model from the Open Pretrained Transformer family, with promising results. The watermarking framework is computationally simple to verify, has a low false positive detection rate, and degrades gracefully under attack. The framework can be retrofitted to any existing model that generates text via sampling from the next token distribution without retraining. While some questions remain about the best way to implement and tune the watermarking framework against generative attacks, the potential benefits are clear, and it could be crucial for ensuring that machine-generated text is used responsibly and ethically.

Bullet points

  • Large language models (LLMs) like ChatGPT can write documents, create code, and answer questions, but they also pose potential risks, such as social engineering, election manipulation, and the creation of fake news.
  • Researchers have proposed a watermarking framework that embeds signals into generated text, making it detectable without knowledge of the model parameters or access to the language model API.
  • The watermarking framework is computationally simple to verify, has a low false positive detection rate, and degrades gracefully under attack.
  • The framework can be retrofitted to any existing model that generates text via sampling from the next token distribution without retraining.
  • The potential benefits of the watermarking framework are clear, and it could be crucial for ensuring that machine-generated text is used responsibly and ethically.
  • While some questions remain about the best way to implement and tune the watermarking framework against generative attacks, the potential benefits are clear.

State of The AI Art March 2023

The Secret Code: How Watermarking Can Make Large Language Models Safe

Protecting Our Digital World: Watermarking chatGPT

A New Era in AI: Watermarking Large Language Models for Safety and Security

In a world where large language models (LLMs) like ChatGPT can write documents, create code, and answer questions, there’s a growing concern about the potential harm they can cause. The risks are real, from social engineering and election manipulation campaigns to the creation of fake news and web content. But what if there was a way to mitigate these risks?

State of The AI Art March 2023

Enter the “watermarking framework” proposed by a group of researchers. This involves embedding signals into generated text that are invisible to humans but algorithmically detectable. The watermark selects a randomized set of “green” tokens before a word is generated and softly promotes using those tokens during sampling.

Battling the Dark Side of Language Models: A Watermarking Revolution

The best part? The watermark can be detected without knowledge of the model parameters or access to the language model API. This allows for open-source detection algorithms, which are cheap and fast because the LLM doesn’t need to be loaded or run.

But how effective is this watermarking framework, really?

The researchers tested it using a multi-billion parameter model from the Open Pretrained Transformer family, and the results were promising. The watermark is computationally simple to verify, false positive detections are statistically improbable, and the watermark degrades gracefully under attack.

Of course, some open questions remain about the best way to implement and tune the watermarking framework against generative attacks, but the potential benefits are clear. This framework could be a crucial step toward reducing the harm caused by large language models by enabling the detection and auditing of machine-generated text.

Protecting the Future: A Watermark for Large Language Models

Language model outputs, both with and without the use of a watermark. The watermarked text should have 9 “green” tokens if authored by a person, yet it has 28. The odds of this happening by chance is 6 1014, making us almost likely that this text was created by a computer. Words are distinguished by their color. The model is OPT-6.7B, and it employs multinomial sampling. The watermark parameters are, = (0.25, 2). The prompt is the entire text in blue below.

And there’s one more thing that makes this watermarking framework particularly exciting: its flexibility. It can be retrofitted to any existing model that generates text via sampling from the next token distribution without retraining. And different context-specific δ choices or green list enforcement rules can be used for different kinds of text or models, all while using the same downstream watermark detector.

The Key to Responsible AI: A Watermarking System for Large Language Models

In short, the watermarking framework proposed by these researchers could be a game-changer in mitigating the potential harm caused by large language models. While some questions remain to be answered, the potential benefits are clear. Furthermore, with careful implementation and tuning, this framework could be crucial for ensuring that machine-generated text is used responsibly and ethically.

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Keywords: computer vision, Artificial Intelligence, Machine Learning, AI art, art, wombo dream, digital art, Dalle 2, Imagen, wombo ai, Parti, 3D point cloud, diffusion models, generative art, wombo art, photographic quality, img by AI system, AI art generator, text to art generator, 3D, midjourney, dalle2, stablediffusion, Dalle 3, vision-language model, AI artists

https://arxiv.org/pdf/2301.10226.pdf

Project Page:

https://arxiv.org/pdf/2301.10226.pdf

A Watermark for Large Language Models
John Kirchenbauer Jonas Geiping Yuxin Wen Jonathan Katz Ian Miers Tom Goldstein
Machine Learning
Artificial Intelligence
Data Science
ChatGPT
Technology
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