# Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination

arXiv: https://arxiv.org/abs/2306.06331 · HTML: https://mlsanity.com/paper/2306.06331/

## Key facts

- Cited by 13 papers in the map (data through 14 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.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Builds on (most cited first, 4 of 4)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models](https://mlsanity.com/paper/2305.12199/) (2023)

## Built on by (most cited first, 13 of 13)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Tool Learning with Large Language Models: A Survey](https://mlsanity.com/paper/2405.17935/) (2024)
- [When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks](https://mlsanity.com/paper/2311.08993/) (2023)
- [A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time](https://mlsanity.com/paper/2309.07418/) (2023)
- [Causal Evaluation of Language Models](https://mlsanity.com/paper/2405.00622/) (2024)
- [Evaluation of LLMs for mathematical problem solving](https://mlsanity.com/paper/2506.00309/) (2025)
- [ChatGPT is Good but Bing Chat is Better for Vietnamese Students](https://mlsanity.com/paper/2307.08272/) (2023)
- [Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems](https://mlsanity.com/paper/2410.14074/) (2024)
- [MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training](https://mlsanity.com/paper/2502.20855/) (2025)
- [V-Math: An Agentic Approach to the Vietnamese National High School Graduation Mathematics Exams](https://mlsanity.com/paper/2509.12251/) (2025)
- [AI-assisted Learning for Electronic Engineering Courses in High Education](https://mlsanity.com/paper/2311.01048/) (2023)
- [Assessment of Evolving Large Language Models in Upper Secondary Mathematics](https://mlsanity.com/paper/2504.12347/) (2025)
- [Generative AI performance in core undergraduate mathematics: a curriculum-level case study](https://mlsanity.com/paper/2509.13359/) (2025)

## 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.