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Enhancing educational Q&A systems using a Chaotic Fuzzy Logic-Augmented large language model
Haoyuan Chen1, Nuobei Shi1,2, Ling Chen3
1Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
Introduction:
Online question-and-answer (Q&A) 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 Q&A System (CHAQS) to navigate the complexities associated with intelligent Q&A 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 Q&A systems, demonstrating the effectiveness of combining advanced tuning techniques and fuzzy logic for improved model precision and adaptability.
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