Adaptation Protocol

Adaptation protocols are methods for modifying pre-trained models to improve performance on specific tasks or datasets, aiming to balance accuracy with robustness and safety. Current research focuses on optimizing these protocols by exploring different adaptation techniques (e.g., parameter updates, reward modeling, in-context prompting), detecting and mitigating concept drift, and addressing issues like catastrophic forgetting and simplicity bias. This work is crucial for enhancing the reliability and generalizability of machine learning models across diverse applications, particularly in dynamic environments where continuous adaptation is necessary.