REVIEW 4 cited by
Who Wrote this Code? Watermarking for Code Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 4 Pith papers
-
Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
A trigger-tag watermark embedded by fine-tuning lets modified LLMs mark their own phishing outputs for cheap detection.
-
Optimizing Token Choice for Code Watermarking: An RL Approach
An RL-trained policy adaptively biases token choices to watermark LLM-generated code while preserving executable behavior.
-
Detecting LLM-generated Code with Subtle Modification by Adversarial Training
CodeGPTSensor+, trained with adversarial samples that combine identifier renaming and structure transformation, is substantially more robust to subtle modifications of LLM-generated code than the original CodeGPTSensor.
-
Enhancing Watermarking Quality for LLMs via Contextual Generation States Awareness
A context-aware plug-in for LLM watermarking that skips or weakens watermarks on semantically critical tokens, improving task accuracy at similar detection rates.
Discussion (0). Sign in to comment.