  Since the remarkable generation performance of large language models raised
ethical and legal concerns, approaches to detect machine-generated text by
embedding watermarks are being developed. However, we discover that the
existing works fail to function appropriately in code generation tasks due to
the task's nature of having low entropy. Extending a logit-modifying watermark
method, we propose Selective WatErmarking via Entropy Thresholding (SWEET),
which enhances detection ability and mitigates code quality degeneration by
removing low-entropy segments at generating and detecting watermarks. Our
experiments show that SWEET significantly improves code quality preservation
while outperforming all baselines, including post-hoc detection methods, in
detecting machine-generated code text. Our code is available in
https://github.com/hongcheki/sweet-watermark.
