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- W4378465438 abstract "Large language models for code have recently shown remarkable performance in generating executable code. However, this rapid advancement has been accompanied by many legal and ethical concerns, such as code licensing issues, code plagiarism, and malware generation, making watermarking machine-generated code a very timely problem. Despite such imminent needs, we discover that existing watermarking and machine-generated text detection methods for LLMs fail to function with code generation tasks properly. Hence, in this work, we propose a new watermarking method, SWEET, that significantly improves upon previous approaches when watermarking machine-generated code. Our proposed method selectively applies watermarking to the tokens with high enough entropy, surpassing a defined threshold. The experiments on code generation benchmarks show that our watermarked code has superior quality compared to code produced by the previous state-of-the-art LLM watermarking method. Furthermore, our watermark method also outperforms DetectGPT for the task of machine-generated code detection." @default.
- W4378465438 created "2023-05-27" @default.
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- W4378465438 date "2023-05-24" @default.
- W4378465438 modified "2023-09-27" @default.
- W4378465438 title "Who Wrote this Code? Watermarking for Code Generation" @default.
- W4378465438 doi "https://doi.org/10.48550/arxiv.2305.15060" @default.
- W4378465438 hasPublicationYear "2023" @default.
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