AutoTutor 遇见大型语言模型：一个具备丰富教学法和护栏的语言模型导师
AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails
AI总结：
本文探讨用大型语言模型构建智能辅导系统，提出结合手工教学设计与有限状态转换器的混合方法MWPTutor，在数学应用题上辅导效果优于自由生成的GPT-4。
摘要：
大型语言模型（LLMs）在教育领域已有多种应用场景，从自动问题生成到作文评估。在本文中，我们探讨了使用大型语言模型（LLMs）来构建智能辅导系统（Intelligent Tutoring Systems）的潜力。LLMs 的一个常见缺陷是它们会偏离预期的教学策略，例如向学生泄露答案，并且总体上无法提供保证。我们认为，虽然带有特定护栏的 LLMs 可以取代学科专家，但为了获得最佳学习效果，整体教学设计仍需手工精心打造。基于这一原则，我们创建了一个名为 MWPTutor 的端到端辅导系统样本，该系统利用 LLMs 来填充预定义有限状态转换器的状态空间。这种方法保留了学习科学家多年来发展的传统辅导系统的结构和教学法，同时引入了基于 LLM 方法的额外灵活性。通过对两个基于数学应用题的数据集进行人工评估研究，我们表明，与经过指令微调但自由生成的 GPT-4 相比，我们的混合方法获得了更好的整体辅导评分。MWPTutor 完全模块化，为社区通过改进各个模块或采用其可遵循的不同教学策略来提升其性能开辟了空间。
英文摘要：
Large Language Models (LLMs) have found several use cases in education, ranging from automatic question generation to essay evaluation. In this paper, we explore the potential of using Large Language Models (LLMs) to author Intelligent Tutoring Systems. A common pitfall of LLMs is their straying from desired pedagogical strategies such as leaking the answer to the student, and in general, providing no guarantees. We posit that while LLMs with certain guardrails can take the place of subject experts, the overall pedagogical design still needs to be handcrafted for the best learning results. Based on this principle, we create a sample end-to-end tutoring system named MWPTutor, which uses LLMs to fill in the state space of a pre-defined finite state transducer. This approach retains the structure and the pedagogy of traditional tutoring systems that has been developed over the years by learning scientists but brings in additional flexibility of LLM-based approaches. Through a human evaluation study on two datasets based on math word problems, we show that our hybrid approach achieves a better overall tutoring score than an instructed, but otherwise free-form, GPT-4. MWPTutor is completely modular and opens up the scope for the community to improve its performance by improving individual modules or using different teaching strategies that it can follow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
