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DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction

arXiv 2405.12100 ↗First posted 20 May 2024cs.CLcited by 0

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Abstract (arXiv)

Math world problems correction(MWPC) is a novel task dedicated to rectifying reasoning errors in the process of solving mathematical problems. In this paper, leveraging the advancements in large language models (LLMs), we address two key objectives:(1) Distinguishing between mathematical reasoning and error correction; (2) Exploring strategies to enhance the error correction capabilities of LLMs in mathematics to solve MWPC task. We noticed that, in real-time education,assisting students in recognizing their mistakes is more crucial than simply providing correct answers. However, current research tends to prioritize obtaining accurate solutions to math problems rather than correcting potentially incorrect ones. Therefore, we modify the research paradigm, demonstrating that improving mathematical reasoning abilities does not equate to mastery in error correction. Meanwhile, we propose a novel method called diagnostic-oriented promping(DOP) aimed at facilitating LLMs to excel in error correction. In experiments, DOP has shown outstanding performance, highlighting its significant impact. We argue that in mathematical education, the demand for outstanding correctors surpasses that for proficient reasoners. Codes and data are available on https://github.com/ChenhaoEcnuCS/Reason-Correct.

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21 of 30 references.

References · 21

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  6. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  7. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  8. 2023Baichuan 2: Open Large-scale Language Modelscited by 670
  9. 2023WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instructcited by 625
  10. 2022Large Language Models Are Reasoning Teacherscited by 329
  11. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 312
  12. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 238
9 more
  1. 2023MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problemscited by 131
  2. 2023Can Large Language Models Really Improve by Self-critiquing Their Own Plans?cited by 108
  3. 2023GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problemscited by 89
  4. 2023Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Taskscited by 84
  5. 2023Improving Large Language Model Fine-tuning for Solving Math Problemscited by 49
  6. 2023Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughtscited by 27
  7. 2023Beyond English: Evaluating LLMs for Arabic Grammatical Error Correctioncited by 12
  8. 2023ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questionscited by 8
  9. 2023FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotationcited by 5

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  1. 2024Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning
  2. 2024Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
  3. 2024LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought
  4. 2025SMRC: Aligning Large Language Models with Student Reasoning for Mathematical Error Correction
  5. 2025Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework
  6. 2024Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems
  7. 2025Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning
  8. 2024Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models