VidTutorAssistant：自动响应编程教程问题
VidTutorAssistant: Automating Responses to Programming Tutorial Questions
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中文总结 AI 辅助
VidTutorAssistant是一个网络平台，利用检索增强生成和GPT-4自动回答编程教程视频下的观众问题，在440条评论上实现高准确率与回答质量。
中文摘要 AI 辅助
YouTube上的编程教程视频是软件开发者与学生的重要信息资源，其评论区已演变为观众提出后续问题的活跃空间。然而，这些问题的数量往往超出内容创作者能够处理的限度，使学习者无法获得所需的澄清。我们提出VidTutorAssistant，一个自动化响应编程视频教程观众问题的网络平台。VidTutorAssistant实现了一个检索增强生成流水线，提取视频转录文本，然后对其进行分段并嵌入。随后，它将每条观众评论分类为问题或非问题，通过余弦相似度检索与每个已识别问题最相关的转录片段，并使用LLM（GPT-4）生成答案，同时以检索到的转录片段作为上下文来支撑响应。我们通过一项研究验证VidTutorAssistant，该研究使用了从7,522个Python和Java教程中提取的105,553条评论的更大数据集中选出的440条用户评论子集。VidTutorAssistant在多个标准上进行了评估：a) 识别视频中编程语言的能力，达到0.99的准确率；b) 将评论分类为问题与非问题的能力，达到0.96的准确率；c) 生成正确且完整答案的能力，产生98%的正确响应和99.5%的完整响应，而原始创作者的回答分别为89%和90%。
英文摘要
Programming tutorial videos on YouTube are an important information resource for software developers and students, and their comment sections have evolved into active spaces where viewers ask follow-up questions. The volume of these questions, however, often exceeds what content creators can address, leaving learners without the clarifications they need. We present VidTutorAssistant, a web platform that automates responses to viewer questions on programming video tutorials. VidTutorAssistant implements a retrieval-augmented generation pipeline that extracts a video's transcript, then segments it and embeds it. It then classifies each viewer comment as being a question or non-question, retrieves the most relevant transcript segments to each identified question via cosine similarity, and then generates an answer to the question using an LLM (GPT-4), while grounding the response using the retrieved transcript segments as context. We validate VidTutorAssistant through a study on a subset of 440 user comments selected from a larger dataset of 105,553 comments extracted from 7,522 Python and Java tutorials. VidTutorAssistant is evaluated on various criteria: a) its ability to identify the programming language in a video, achieving a 0.99 accuracy; b) its ability to classify comments into questions and non-questions, reaching a 0.96 accuracy; and c) its ability to produce correct and complete answers to questions, producing 98% correct and 99.5% complete responses, compared with 89% and 90% for the original creators' answers.
发表机构
- King Abdulaziz University（阿卜杜勒阿齐兹国王大学）
- Florida State University（佛罗里达州立大学）
- University of Jeddah（吉达大学）
机构由 AI 辅助整理，请以论文原文为准。
