mlsanityData to 14 Oct 2026

MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs

arXiv 2503.04291 ↗First posted 6 Mar 2025cs.AIcited by 1

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Abstract (arXiv)

We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 6 Mar 2025.

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7 of 13 references.

References · 7

  1. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  2. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  3. 2015Pointer Networkscited by 895
  4. 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 520
  5. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 485
  6. 2024LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thoughtcited by 28
  7. 2024Large Language Models as Partners in Student Essay Evaluationcited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressionscited by 4

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  1. 2024DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction
  2. 2024Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning
  3. 2025MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection
  4. 2024Ask-Before-Detection: Identifying and Mitigating Conformity Bias in LLM-Powered Error Detector for Math Word Problem Solutions
  5. 2025From Correctness to Comprehension: AI Agents for Personalized Error Diagnosis in Education
  6. 2023Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions
  7. 2024ProcessBench: Identifying Process Errors in Mathematical Reasoning
  8. 2025Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning