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arXiv · 2407.21170

Decomposed Prompting to Answer Questions on a Course Discussion Board

Brandon Jaipersaud·Paul Zhang·Jimmy Ba·Andrew Petersen·Lisa Zhang·Michael R. Zhang
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Abstract

We propose and evaluate a question-answering system that uses decomposed prompting to classify and answer student questions on a course discussion board. Our system uses a large language model (LLM) to classify questions into one of four types: conceptual, homework, logistics, and not answerable. This enables us to employ a different strategy for answering questions that fall under different types. Using a variant of GPT-3, we achieve $81\%$ classification accuracy. We discuss our system's performance on answering conceptual questions from a machine learning course and various failure modes.

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Brandon Jaipersaud, Paul Zhang, Jimmy Ba, Andrew Petersen, Lisa Zhang, Michael R. Zhang. 2024-07-30. Decomposed Prompting to Answer Questions on a Course Discussion Board. https://doi.org/10.1007/978-3-031-36336-8_33

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