Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination
On the map: LLM reasoning → Mathematical reasoning
Abstract (arXiv)
This study offers a complete analysis of ChatGPT's mathematics abilities in responding to multiple-choice questions for the Vietnamese National High School Graduation Examination (VNHSGE) on a range of subjects and difficulty levels. The dataset included 250 questions divided into four levels: knowledge (K), comprehension (C), application (A), and high application (H), and it included ten themes that covered diverse mathematical concepts. The outcomes demonstrate that ChatGPT's performance varies depending on the difficulty level and subject. It performed best on questions at Level (K), with an accuracy rate of 83%; but, as the difficulty level rose, it scored poorly, with an accuracy rate of 10%. The study has also shown that ChatGPT significantly succeeds in providing responses to questions on subjects including exponential and logarithmic functions, geometric progression, and arithmetic progression. The study found that ChatGPT had difficulty correctly answering questions on topics including derivatives and applications, spatial geometry, and Oxyz spatial calculus. Additionally, this study contrasted ChatGPT outcomes with Vietnamese students in VNHSGE and in other math competitions. ChatGPT dominated in the SAT Math competition with a success rate of 70%, followed by VNHSGE mathematics (58.8%). However, its success rates were lower on other exams, such as AP Statistics, the GRE Quantitative, AMC 10, AMC 12, and AP Calculus BC. These results suggest that ChatGPT has the potential to be an effective teaching tool for mathematics, but more work is needed to enhance its handling of graphical data and address the challenges presented by questions that are getting more challenging.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. Builds on 4 of its 33 references that are in the map. First posted on arXiv 10 Jun 2023.
Builds on
4 of 33 references.
References · 4
- 2023GPT-4 Technical Reportcited by 18,132
- 2023Mathematical Capabilities of ChatGPTcited by 263
- 2023A Categorical Archive of ChatGPT Failurescited by 167
- 2023VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Modelscited by 15
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Citing papers per year: 2023: 5; 2024: 3; 2025: 5.
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- 2024Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems17 Oct 2024
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- 2023A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time14 Sep 2023
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1 more
- 2023A Survey on Evaluation of Large Language Models6 Jul 2023
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- 2025Assessment of Evolving Large Language Models in Upper Secondary Mathematicscited by 0
- 2025Assessment of Evolving Large Language Models in Upper Secondary Mathematics15 Apr 2025
- 2024Causal Evaluation of Language Models1 May 2024
- 2023A Survey on Evaluation of Large Language Modelscited by 1,227
- 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
- 2023A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Timecited by 30
- 2025Evaluation of LLMs for mathematical problem solvingcited by 7
- 2023ChatGPT is Good but Bing Chat is Better for Vietnamese Studentscited by 5
- 2024Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systemscited by 2
- 2025MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Trainingcited by 1
- 2023AI-assisted Learning for Electronic Engineering Courses in High Educationcited by 0
- 2025Generative AI performance in core undergraduate mathematics: a curriculum-level case studycited by 0
- 2025Generative AI performance in core undergraduate mathematics: a curriculum-level case study15 Sep 2025
- 2025Evaluation of LLMs for mathematical problem solving30 May 2025
- 2025MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training28 Feb 2025
- 2024Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems17 Oct 2024
- 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks15 Nov 2023
- 2023AI-assisted Learning for Electronic Engineering Courses in High Education2 Nov 2023
- 2023A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time14 Sep 2023
- 2023ChatGPT is Good but Bing Chat is Better for Vietnamese Students17 Jul 2023
- 2023A Survey on Evaluation of Large Language Models6 Jul 2023
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