부산대학교 도서관

To enhance the accuracy and reliability of the response of large language models (LLMs) in professional domain-specific question answering, this paper proposes an approach to enhance the capabilities of LLMs by prompting them to "think twice." This methodology encourages a second-pass processing, allowing for deeper analysis and more refined outputs. We developed a prototype-logic-inspired query optimizer (PQO) to enhance the response of LLM. It refines initial queries using few-shot learning and self-reflection, leading to more insightful questions. Our experimental results show that the proposed approach obtains empirically validated answers.