用BERT微调大学课程问答系统，提升学生信息获取效率。
Fine-Tuning BERT for Domain-Specific Question Answering: Toward Educational NLP Resources at University Scale
- 基于1203个课程问答对微调BERT模型
- 准确率与F1分数均优于通用模型
- 为高校教育智能系统提供可扩展范例
针对科学问答任务，现有研究多聚焦聊天机器人系统，较少关注基础模型在特定领域的推理能力优化。本研究为利默里克大学电子与计算机工程系开发了课程信息聊天机器人，构建了包含1,203个问题-答案对的定制数据集（SQuAD格式），结合手册原文、人工标注和合成生成方式补充数据。使用PyTorch对BERT（Devlin et al., 2019）进行微调，并以精确匹配（Exact Match）和F1分数评估性能。结果表明，即使进行有限度的微调，也能显著提升假设构建与知识提取能力，验证了将基础模型适配教育领域可行性。尽管已有BioBERT和SciBERT等针对生物医学和科学文献的领域专用变体，但尚无专用于大学课程材料的模型。本研究填补该空白，证明通过学术问答对微调BERT可获得有效成果，为构建首个高校级专用问答模型提供了路径，助力实现自主教育知识系统。
原文摘要 · Abstract (English)
Prior work on scientific question answering has largely emphasized chatbot-style systems, with limited exploration of fine-tuning foundation models for domain-specific reasoning. In this study, we developed a chatbot for the University of Limerick's Department of Electronic and Computer Engineering to provide course information to students. A custom dataset of 1,203 question-answer pairs in SQuAD format was constructed using the university book of modules, supplemented with manually and synthetically generated entries. We fine-tuned BERT (Devlin et al., 2019) using PyTorch and evaluated performance with Exact Match and F1 scores. Results show that even modest fine-tuning improves hypothesis framing and knowledge extraction, demonstrating the feasibility of adapting foundation models to educational domains. While domain-specific BERT variants such as BioBERT and SciBERT exist for biomedical and scientific literature, no foundation model has yet been tailored to university course materials. Our work addresses this gap by showing that fine-tuning BERT with academic QA pairs yields effective results, highlighting the potential to scale towards the first domain-specific QA model for universities and enabling autonomous educational knowledge systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。