Knowledge Update DMIS

Knowledge Update for Direct Memory Interface Systems (KU-DMIS) focuses on efficiently updating the knowledge within large language models (LLMs) to address the issue of outdated or inaccurate information. Current research emphasizes techniques like parameter-efficient fine-tuning and direct preference optimization to selectively modify model parameters, avoiding costly retraining. These advancements aim to improve the accuracy and timeliness of LLMs across various applications, such as question answering and text-to-SQL query generation in domains like healthcare, ultimately enhancing the reliability and utility of these powerful AI systems.