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

Frontiers in Artificial Intelligence Pub Date : 2024-08-08 DOI:10.3389/frai.2024.1404940

Haoyuan Chen, Nuobei Shi, Ling Chen, Raymond S. T. Lee

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Abstract

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.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.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.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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利用混沌模糊逻辑增强大型语言模型改进教育问答系统

在线问答（Q&A）平台经常需要大量的人力资源支持。本研究提出了一种新颖的定制大型语言模型（LLM）方法，称为基于混沌LLM的教育问答系统（CHAQS），以解决与教育领域智能问答系统相关的复杂问题。它使用了一个由超过383,000对教育数据组成的庞大数据集，一个复杂的微调过程，包括p-tuning v2、低秩自适应（LRA），以及以开源大型语言模型ChatGLM为基准模型的参数冻结策略。实验结果表明，与其他模型相比，精确度提高了 5.12%，召回率提高了 11%，F1 分数提高了 8%。这些结果表明，CHAQS 方法显著提高了教育问答系统的性能，证明了结合高级调整技术和模糊逻辑提高模型精确度和适应性的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Frontiers in Artificial Intelligence

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