Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains
Year: 2024
Type: conference-paper
Abstract: Question generation in education is a time-consuming and cognitively demanding task, as it requires creating questions that are both contextually relevant and pedagogically sound.Current automated question generation methods often generate questions that are out of context.In this work, we explore advanced techniques for automated question generation in educational contexts, focusing on In-Context Learning (ICL), Retrieval-Augmented Generation (RAG), and a novel Hybrid Model that merges both methods.We implement GPT-4 for ICL using few-shot examples and BART with a retrieval module for RAG.The Hybrid Model combines RAG and ICL to address these issues and improve question quality.Evaluation is conducted using automated metrics, followed by human evaluation metrics.Our results show that both the ICL approach and the Hybrid Model consistently outperform other methods, including baseline models, by generating more contextually accurate and relevant questions.
Authors: Subhankar Maity, Aniket Deroy, Sudeshna Sarkar
Institution: Indian Institute of Technology Kharagpur
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412638228