Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors
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Date
2024-11
Publication Type
Conference Paper
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yes
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Abstract
Large language models (LLMs) offer many opportunities to scale high-quality personalized tutoring. A promising approach is to build dialog tutoring models to scaffold students’ problem-solving. However, even though existing models perform well in solving reasoning questions, they can struggle to precisely detect student’s errors and tailor their feedback to these errors. Inspired by real-world teaching practice where teachers identify student errors and customize their response based on them, we focus on verifying student solutions and show how grounding to such verification improves the overall quality of tutor response generation. We collect a dataset of 1,002 stepwise math reasoning chains with the first error step annotated by teachers. We show empirically that finding the mistake in a student solution is challenging for current models. We propose and evaluate several verifiers for detecting these errors. Using both automatic and human evaluation we show that the student solution verifiers steer the generation model towards highly targeted responses to student error which are more often correct with less hallucinations compared to existing baselines. The benchmark dataset and code will be released openly.
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Publication status
published
Book title
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Journal / series
Volume
Pages / Article No.
8386 - 8411
Publisher
Association for Computational Linguistics
Event
29th Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)
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Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09590 - Kapur, Manu / Kapur, Manu
09684 - Sachan, Mrinmaya / Sachan, Mrinmaya
02219 - ETH AI Center / ETH AI Center