EduVidQA：基于讲座视频生成并评估学生问题的长文本答案
EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos
AI总结：
本文提出EduVidQA数据集与长视频问答任务，构建5252个问答对，结合学生偏好和6个MLLM基准评估，揭示任务挑战并为教育NLP研究奠定基准。
摘要：
随着数字平台重新定义教育范式，确保互动性对于有效学习仍然至关重要。本文探索使用多模态大语言模型（MLLM）自动回答来自在线讲座的学生问题——这是一项具有现实意义的新型问答任务。我们介绍了 EduVidQA 数据集，其中包含来自296个计算机科学视频的5252个问答对（包括合成问答对和真实世界问答对），涵盖不同主题和难度级别。为理解数据集需求和任务评估，我们实证研究了学生的定性偏好，并将其作为对这一研究方向的重要贡献。我们的基准实验包含6个最先进的MLLM，借此研究合成数据用于微调的有效性，并展示该任务的挑战性。我们同时使用基于文本的指标和定性指标评估模型，从而呈现模型性能的细致视角，这对未来工作至关重要。本工作不仅为这一重要问题建立了基准，也为教育自然语言处理领域的未来研究开辟了令人振奋的方向。
英文摘要：
As digital platforms redefine educational paradigms, ensuring interactivity remains vital for effective learning. This paper explores using Multimodal Large Language Models (MLLMs) to automatically respond to student questions from online lectures - a novel question answering task of real world significance. We introduce the EduVidQA Dataset with 5252 question-answer pairs (both synthetic and real-world) from 296 computer science videos covering diverse topics and difficulty levels. To understand the needs of the dataset and task evaluation, we empirically study the qualitative preferences of students, which we provide as an important contribution to this line of work. Our benchmarking experiments consist of 6 state-of-the-art MLLMs, through which we study the effectiveness of our synthetic data for finetuning, as well as showing the challenging nature of the task. We evaluate the models using both text-based and qualitative metrics, thus showing a nuanced perspective of the models' performance, which is paramount to future work. This work not only sets a benchmark for this important problem, but also opens exciting avenues for future research in the field of Natural Language Processing for Education.
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。
