Model Weight

Model weights, the numerical parameters within machine learning models, are a central focus of current research, with objectives ranging from improving model performance and security to understanding their legal and ethical implications. Research emphasizes efficient training and aggregation techniques, particularly within federated learning and for large language models (LLMs), often employing methods like weight averaging, sparse formats, and parameter-efficient fine-tuning. The study of model weights is crucial for advancing model interpretability, security (detecting hidden malware), and responsible development and deployment of AI systems, impacting both scientific understanding and practical applications.