Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets
Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training examples verbatim and without authorship attribution, raising legal and ethical concerns around plagiarism and license compliance. Classical fingerprint-based plagiarism detectors, such as Winnowing, remain highly effective, yet the inspection requires comparing fragments of code to the entire training set, and their linear-time search makes them impractical for the billion-scale corpora used to train modern code LLMs. To bridge this gap, we introduce SourceTracker, a 300M-parameter encoder tailored for code retrieval, together with a hybrid two-st...
| Authors | Andrea Gurioli, Davide D'Ascenzo, Federico Pennino et al. |
|---|---|
| Primary category | cs.SE |
| Published | 27 May 2026 |
| Last updated at source | 8 October 2026 |
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