Core Concepts
The author presents an iterative enhancement framework, ILearner-LLM, to improve learnersourced question explanations using large language models. By iteratively generating and evaluating explanations, the framework aims to enhance the quality of student-aligned explanations.
Abstract
The content explores the application of large language models in educational contexts through learnersourcing multiple-choice question explanations. The ILearner-LLM framework is introduced to iteratively generate and evaluate high-quality explanations for student-generated questions across various academic subjects. Experimental results demonstrate notable improvements in explanation quality and alignment with student-written explanations.
Key points:
- Introduction of ILearner-LLM framework for enhancing learnersourced question explanations.
- Importance of generating high-quality student-aligned explanations.
- Utilization of large language models like LLaMA2-13B and GPT-4 for explanation generation.
- Iterative process involving explanation generation and evaluation to improve explanation quality.
- Comparison of different models and fine-tuning strategies for better performance in explanation generation and evaluation.
Stats
"Large language models exhibit superior capabilities in processing and understanding language."
"Experimental results demonstrate the effectiveness of ILearner-LLM on generating higher quality explanations."
"Our findings represent a promising path to enrich the learnersourcing experience for students."
Quotes
"Large language models exhibit superior capabilities in processing and understanding language."
"Our findings represent a promising path to enrich the learnersourcing experience for students."