Summary of Freqmark: Frequency-based Watermark For Sentence-level Detection Of Llm-generated Text, by Zhenyu Xu and Kun Zhang and Victor S. Sheng
FreqMark: Frequency-Based Watermark for Sentence-Level Detection of LLM-Generated Text
by Zhenyu Xu, Kun Zhang, Victor S. Sheng
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FreqMark technique embeds frequency-based watermarks in Large Language Model (LLM)-generated text, enabling accurate identification of LLM-generated content even in mixed-text scenarios. By leveraging periodic signals to guide token selection, FreqMark creates a detectable watermark that can be analyzed using Short-Time Fourier Transform (STFT). The method shows strong detection capabilities against various attack scenarios, outperforming existing detection methods with an AUC improvement of up to 0.98. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to identify text generated by Large Language Models (LLMs) by adding special marks or “watermarks” that can be detected later. The watermark is made by changing the frequency of words in a specific pattern, making it hard for others to remove without leaving traces. This technique helps stop the misuse of LLMs for spreading false information. |
Keywords
» Artificial intelligence » Auc » Large language model » Token
