AI & Enterprise
KAIST develops tech to boost safety of custom AI while preserving performance
KAIST said a research team developed a learning framework designed to improve the safety of customised AI trained on personal or corporate documents while keeping task performance. The Buffer-and-Reinforce approach applies a temporary buffering module during fine-tuning to block malicious data from affecting the core model, then removes it and adds a safety reinforcement module using QR decomposition. In tests, the share of dangerous answers fell to about 8 percent from about 18 percent.
