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  • Zachary Levonian, Chenglu Li, Wangda Zhu...
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    Oct 29th, 2023
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    journalArticle
    Zachary Levonian, Chenglu Li, Wangda Zhu...
    Oct 29th, 2023

    For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts. However, LLM responses to math questions can be incorrect or mismatched to the educational context - such as being misaligned with a...

  • Zachary Levonian, Chenglu Li, Wangda Zhu...
    |
    Oct 29th, 2023
    |
    journalArticle
    Zachary Levonian, Chenglu Li, Wangda Zhu...
    Oct 29th, 2023

    For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts. However, LLM responses to math questions can be incorrect or mismatched to the educational context - such as being misaligned with a...

  • Zachary Levonian, Chenglu Li, Wangda Zhu...
    |
    Oct 4th, 2023
    |
    preprint
    Zachary Levonian, Chenglu Li, Wangda Zhu...
    Oct 4th, 2023

    For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts. However, LLM responses to math questions can be incorrect or mismatched to the educational context - such as being misaligned with a...

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