Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code

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作者：
University of Houston, Houston, United States [1 ]
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来源：
AIware - Proc. ACM Int. Conf. AI-Powered Softw., Co-located: ESEC/FSE | 1600年 / 59-64期
关键词：
Backdoors - Embeddings - Language model - Large language model of code - Model embedding - Modeling parameters - Safe AI - Software development practices - Trojan detections - Trojans;
D O I：
1st ACM International Conference on AI-Powered Software, AIware 2024, co-located with the ACM International Conference on the Foundations of Software Engineering, FSE 2024
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摘要：
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