Decomposed Prompting to Answer Questions on a Course Discussion Board
<|reference_start|>Decomposed Prompting to Answer Questions on a Course Discussion Board: 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.<|reference_end|>
@article{jaipersaud2024decomposed,
    title={Decomposed Prompting to Answer Questions on a Course Discussion Board},
    author={Brandon Jaipersaud, Paul Zhang, Jimmy Ba, Andrew Petersen, Lisa Zhang,
  Michael R. Zhang},
    journal={In: Artificial Intelligence in Education. AIED 2023.
  Communications in Computer and Information Science, vol 1831. Springer, Cham},
    year={2024},
    doi={10.1007/978-3-031-36336-8_33},
    archivePrefix={arXiv},
    eprint={2407.21170},
    primaryClass={cs.CL cs.HC}
}