Educational chatbots are a promising tool for assisting student learning.
However, the development of effective chatbots in education has been
challenging, as high-quality data is seldom available in this domain. In this
paper, we propose a framework for generating synthetic teacher-student
interactions grounded in a set of textbooks. Our approaches capture one aspect
of learning interactions where curious students with partial knowledge
interactively ask a teacher questions about the material in the textbook. We
highlight various quality criteria that such dialogues should fulfill and
compare several approaches relying on either prompting or fine-tuning large
language models. We use synthetic dialogues to train educational chatbots and
show benefits of further fine-tuning in different educational domains. However,
human evaluation shows that our best data synthesis method still suffers from
hallucinations and tends to reiterate information from previous conversations.
Our findings offer insights for future efforts in synthesizing conversational
data that strikes a balance between size and quality. We will open-source our
data and code.

我们提出了一个用于生成基于教科书的综合教师 - 学生互动的框架，通过借助合适的数据合成方法来训练教育聊天机器人，同时对数据的大小和质量进行平衡以解决幻觉和重复信息的问题。

Book2Dial: 从课本中生成师生互动，实现高性价比的教育聊天机器人开发

Book2Dial: Generating Teacher-Student Interactions from Textbooks for  Cost-Effective Development of Educational Chatbots

Collecting high quality conversational data can be very expensive for most
applications and infeasible for others due to privacy, ethical, or similar
concerns. A promising direction to tackle this problem is to generate synthetic
dialogues by prompting large language models. In this work, we use a small set
of expert-written conversations as in-context examples to synthesize a social
conversation dataset using prompting. We perform several thorough evaluations
of our synthetic conversations compared to human-collected conversations. This
includes various dimensions of conversation quality with human evaluation
directly on the synthesized conversations, and interactive human evaluation of
chatbots fine-tuned on the synthetically generated dataset. We additionally
demonstrate that this prompting approach is generalizable to multi-party
conversations, providing potential to create new synthetic data for multi-party
tasks. Our synthetic multi-party conversations were rated more favorably across
all measured dimensions compared to conversation excerpts sampled from a
human-collected multi-party dataset.

使用专家编写的少量对话作为上下文示例，通过提示生成社交对话数据集，可在多方交流任务中创建更多的合成数据。与人类收集的对话相比，合成的多方交流在所有度量维度上都获得了更多的好评。