论文标题
机器提供教育资源 - 利用大型语言模型用于学习
Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing
论文作者
论文摘要
在本文中，我们介绍并评估了创建教育内容的机器程序的概念。 RoboSouring在于众包和大型语言模型的交集，其中大型语言模型的要求代替了人群，取代了人群传统上执行的一些作品。机器提供的包括人类的人类提供启动（输入）以及评估和可能调整产生的人工制品；这些评估也可以用来改善大语言模型。我们建议一个系统来概述机器提供过程。我们进一步研究了使用OpenAI Codex生成的Robosoured和编程练习的评估，在教育背景下进行机器人库的可行性。我们的结果表明，机器提供可以显着减少人类在创建多样化的教育内容的同时，同时保持类似于人类创建的内容的质量。
In this article, we introduce and evaluate the concept of robosourcing for creating educational content. Robosourcing lies in the intersection of crowdsourcing and large language models, where instead of a crowd of humans, requests to large language models replace some of the work traditionally performed by the crowd. Robosourcing includes a human-in-the-loop to provide priming (input) as well as to evaluate and potentially adjust the generated artefacts; these evaluations could also be used to improve the large language models. We propose a system to outline the robosourcing process. We further study the feasibility of robosourcing in the context of education by conducting an evaluation of robosourced and programming exercises, generated using OpenAI Codex. Our results suggest that robosourcing could significantly reduce human effort in creating diverse educational content while maintaining quality similar to human-created content.
