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This paper presents a new methodology for synthetic text detection to ensure ethical use of large-scale language models (LLMs). We reproduce the vulnerabilities of previous studies and propose a new watermarking technique based on them. The experimental results show that the technique is robust to altered generated texts. It shows improved performance compared to the existing aarson watermarking method.
Takeaways, Limitations
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Takeaways: A robust watermarking technique for detecting text generated by LLM is presented, laying a technical foundation for ethical use of LLM. It overcomes the limitations of existing methods and presents improved performance.
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Limitations: Further validation of the proposed method in practical applications is required. Comprehensive experiments on various LLM and text transformation techniques are additionally required. The possibility of overfitting for specific LLMs or text transformations needs to be examined.
