The field of large language models is moving towards enhanced security and privacy measures, with a focus on protecting intellectual property and preventing misuse. Researchers are exploring innovative methods for watermarking and fingerprinting models, as well as developing techniques for detecting and mitigating data memorization risks. Noteworthy papers in this area include: Copyright Protection for Large Language Models, which presents a comprehensive survey of model fingerprinting technologies. DualMark, which introduces a dual-provenance watermarking framework for audio generative models. Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models, which proposes a novel multi-layered privacy protection framework. These advancements have significant implications for the development of responsible and secure large language models.
Advancements in Large Language Model Security and Privacy
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Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
SimInterview: Transforming Business Education through Large Language Model-Based Simulated Multilingual Interview Training System
Optimizing Token Choice for Code Watermarking: A RL Approach
Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
Improving Detection of Watermarked Language Models
MHSNet:An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models
A Study of Privacy-preserving Language Modeling Approaches
DualMark: Identifying Model and Training Data Origins in Generated Audio