SERSEM: Selective Entropy-Weighted Scoring for Membership Inference in Code Language Models

SERSEM: Selective Entropy-Weighted Scoring for Membership Inference in Code Language Models explores A novel framework for detecting data contamination in code LLMs by amplifying specific memorization signals through selective entropy weighting.. Commercial viability score: 7/10 in LLM Security.