eth-nlped/stepverify

arxiv.org/abs/2407.09136 Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors Abstract: Large language models (LLMs) present an opportunity to scale high-quality personalized education to all. A promising approach towards this means is to build dialog tutoring models that scaffold students' problem-solving. However, even though existing LLMs perform well in solving reasoning questions, they struggle to precisely detect student's errors and tailor… See the full description on the dataset page: https://huggingface.co/datasets/eth-nlped/stepverify.

<iframe src="https://silytics.si/embed/item/17e98651-a571-4241-8a4a-aeb6ce47fdfd" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.