Enhancing educational Q&A systems using a Chaotic Fuzzy Logic-Augmented large language model

Introduction Online question-and-answer (QA) platforms are frequently replete with extensive human resource support. This study proposes a novel methodology of a customized large language model (LLM) called Chaotic LLM-based Educational QA System (CHAQS) to navigate the complexities associated with intelligent QA systems for the educational sector. Methods It uses an expansive dataset comprising over 383,000 educational data pairs, an intricate fine-tuning process encompassing p-tuning v2, low-rank adaptation (LRA), and strategies for parameter freezing at an open-source large language model ChatGLM as a baseline model. In addition, Fuzzy Logic is implemented to regulate parameters and the system's adaptability with the Lee Oscillator to refine the model's response variability and precision. Results Experiment results showed a 5.12% improvement in precision score, an 11% increase in recall metric, and an 8% improvement in the F1 score as compared to other models. Discussion These results suggest that the CHAQS methodology significantly enhances the performance of educational QA systems, demonstrating the effectiveness of combining advanced tuning techniques and fuzzy logic for improved model precision and adaptability.