用上下文学习和检索增强生成，自动产出更精准的教育题目。
Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains
- 结合上下文示例与文档检索，提升题目生成相关性。
- 混合模型在自动与人工评估中均优于基线方法。
- 适合教育AI、智能题库开发人员使用。
教育领域的题目自动生成是一项耗时且认知负荷高的任务，需确保题目既与上下文相关又具备教学合理性。现有自动化方法常生成脱离语境的题目。本文探索面向教育场景的先进题目生成技术，聚焦上下文学习（ICL）、检索增强生成（RAG）及一种融合两者的新型混合模型。采用GPT-4实现基于少量示例的ICL，BART搭配检索模块实现RAG。混合模型结合RAG与ICL以改善题目质量。通过自动化指标与人工评估进行验证，结果表明ICL与混合模型在生成更符合上下文、更具相关性的题目方面，持续优于基线模型。
原文摘要 · Abstract (English)
Question generation in education is a time-consuming and cognitively demanding task, as it requires creating questions that are both contextually relevant and pedagogically sound. Current automated question generation methods often generate questions that are out of context. In this work, we explore advanced techniques for automated question generation in educational contexts, focusing on In-Context Learning (ICL), Retrieval-Augmented Generation (RAG), and a novel Hybrid Model that merges both methods. We implement GPT-4 for ICL using few-shot examples and BART with a retrieval module for RAG. The Hybrid Model combines RAG and ICL to address these issues and improve question quality. Evaluation is conducted using automated metrics, followed by human evaluation metrics. Our results show that both the ICL approach and the Hybrid Model consistently outperform other methods, including baseline models, by generating more contextually accurate and relevant questions.
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