Psychological Priming Practice

Psychological priming, in the context of machine learning, involves leveraging prior information or context to guide model behavior and improve performance. Current research focuses on applying priming techniques to enhance large language models (LLMs) for tasks like text summarization and generation, addressing challenges such as length control and harmful output generation. This involves exploring methods like instruction-based tuning, dynamic expert selection, and incorporating priming signals into model architectures to mitigate issues like catastrophic forgetting and the exploitation of "shortcuts" in training data. These advancements aim to improve the reliability, efficiency, and safety of LLMs across various applications.