Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations

• URL: http://arxiv.org/abs/2312.03122v3
• Date: Sat, 20 Jan 2024 15:02:20 GMT
• Title: Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations
• Authors: Tasmia Shahriar, Kelly Ramos and Noboru Matsuda
• Abstract summary: Human educators possess an intrinsic ability to anticipate and seek educational explanations from students. We aim to imbue Intelligent Tutoring Systems with this ability using few-shot learning capability of Large Language Models.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Human educators possess an intrinsic ability to anticipate and seek educational explanations from students, which drives them to pose thought-provoking questions when students cannot articulate these explanations independently. We aim to imbue Intelligent Tutoring Systems with this ability using few-shot learning capability of Large Language Models. Our work proposes a novel prompting technique, Assertion Enhanced Few-Shot Learning, to facilitate the generation of accurate, detailed oriented educational explanations. Our central hypothesis is that, in educational domain, few-shot demonstrations are necessary but not a sufficient condition for quality explanation generation. We conducted a study involving 12 in-service teachers, comparing our approach to Traditional Few-Shot Learning. The results show that Assertion Enhanced Few-Shot Learning improves explanation accuracy by 15% and yields higher-quality explanations, as evaluated by teachers. We also conduct a qualitative ablation study to factor the impact of assertions to provide educator-friendly prompting guidelines for generating explanations in their domain of interest.

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