The content discusses the development of educational chatbots using synthetic teacher-student interactions generated from textbooks. Various approaches are compared, focusing on quality criteria such as Answer Relevance, Informativeness, Coherence, and Factual Consistency. Human evaluation reveals strengths and limitations in the generated dialogues.
The study emphasizes the importance of balancing size and quality in synthesizing conversational data for educational purposes. It explores different models and frameworks to facilitate interactive learning experiences through chatbots based on textbook content. The findings offer insights into improving educational dialogue generation for effective student engagement.
Key points include proposing a framework for generating teacher-student interactions from textbooks, evaluating data synthesis methods for training educational chatbots, and discussing the impact of pre-training on downstream tasks. The study highlights challenges like hallucinations and repetition in synthesized data while showcasing the potential benefits of using such data for pre-training chatbots in various educational domains.
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by Junling Wang... kl. arxiv.org 03-07-2024
https://arxiv.org/pdf/2403.03307.pdfDybere Forespørgsler