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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.21170 |
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| _version_ | 1866909274880344064 |
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| author | Jaipersaud, Brandon Zhang, Paul Ba, Jimmy Petersen, Andrew Zhang, Lisa Zhang, Michael R. |
| author_facet | Jaipersaud, Brandon Zhang, Paul Ba, Jimmy Petersen, Andrew Zhang, Lisa Zhang, Michael R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21170 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decomposed Prompting to Answer Questions on a Course Discussion Board Jaipersaud, Brandon Zhang, Paul Ba, Jimmy Petersen, Andrew Zhang, Lisa Zhang, Michael R. Computation and Language Human-Computer Interaction 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. |
| title | Decomposed Prompting to Answer Questions on a Course Discussion Board |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.21170 |