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Proposes LiSCP, a lightweight stylistic consistency profiling method for robust detection of LLM-generated textual content, focusing on feature stability under adversarial manipulation. Achieves superior performance on in-domain and cross-domain detection with notable robustness.
Andrew Kelley, creator of Zig, argues that LLM-assisted contributions are detectable through distinct mistakes and a 'digital smell,' comparing it to smoking in a non-smoking house.
LLMSniffer is a detection framework that fine-tunes GraphCodeBERT with supervised contrastive learning to distinguish AI-generated code from human-written code, achieving 78% accuracy on GPTSniffer and 94.65% on Whodunit benchmarks. The approach addresses critical challenges in academic integrity and code quality assurance by combining code-structure-aware embeddings with contrastive learning and comment removal preprocessing.