# Evaluating GPT-4's Vision Capabilities on Brazilian University Admission Exams

arXiv: https://arxiv.org/abs/2311.14169 · HTML: https://mlsanity.com/paper/2311.14169/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Evaluating the Performance of Large Language Models on GAOKAO Benchmark](https://mlsanity.com/paper/2305.12474/) (2023)
- [GPT Takes the Bar Exam](https://mlsanity.com/paper/2212.14402/) (2022)
- [Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams](https://mlsanity.com/paper/2303.17003/) (2023)
- [GPT as Knowledge Worker: A Zero-Shot Evaluation of (AI)CPA Capabilities](https://mlsanity.com/paper/2301.04408/) (2023)
- [Sabi'a: Portuguese Large Language Models](https://mlsanity.com/paper/2304.07880/) (2023)
- [BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams](https://mlsanity.com/paper/2307.05410/) (2023)

## Built on by (most cited first, 6 of 6)

- [Sabi'a-2: A New Generation of Portuguese Large Language Models](https://mlsanity.com/paper/2403.09887/) (2024)
- [Juru: Legal Brazilian Large Language Model from Reputable Sources](https://mlsanity.com/paper/2403.18140/) (2024)
- [BLUEX Revisited: Enhancing Benchmark Coverage with Automatic Captioning](https://mlsanity.com/paper/2508.21294/) (2025)
- [Assessing the Capability of LLMs in Solving POSCOMP Questions](https://mlsanity.com/paper/2505.20338/) (2025)
- [MARCA: A Checklist-Based Benchmark for Multilingual Web Search](https://mlsanity.com/paper/2604.14448/) (2026)
- [Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns](https://mlsanity.com/paper/2607.00048/) (2026)

## Abstract (arXiv)

Recent advancements in language models have showcased human-comparable performance in academic entrance exams. However, existing studies often overlook questions that require the integration of visual comprehension, thus compromising the full spectrum and complexity inherent in real-world scenarios. To address this gap, we present a comprehensive framework to evaluate language models on entrance exams, which incorporates both textual and visual elements. We evaluate the two most recent editions of Exame Nacional do Ensino M'edio (ENEM), the main standardized entrance examination adopted by Brazilian universities. Our study not only reaffirms the capabilities of GPT-4 as the state of the art for handling complex multidisciplinary questions, but also pioneers in offering a realistic assessment of multimodal language models on Portuguese examinations. One of the highlights is that text captions transcribing visual content outperform the direct use of images, suggesting that the vision model has room for improvement. Yet, despite improvements afforded by images or captions, mathematical questions remain a challenge for these state-of-the-art models. The code and data used on experiments are available at https://github.com/piresramon/gpt-4-enem.