Knowledge Distortion

Knowledge distortion in artificial intelligence, particularly large language models (LLMs), focuses on understanding and mitigating inaccuracies and biases arising from knowledge acquisition, integration, and manipulation within these systems. Current research investigates phenomena like knowledge entropy decay, expert collapse in multi-task learning architectures (e.g., Mixture-of-Experts), and the unintended consequences of knowledge editing techniques. These studies highlight the need for improved methods to manage knowledge within LLMs, ensuring accuracy, preventing unintended biases, and enhancing the reliability of AI-generated information for various applications.