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The present study aims to explore the capabilities of Language Models (LMs)in tackling high-stakes multiple-choice tests, represented here by the ExameNacional do Ensino M\'edio (ENEM), a multidisciplinary entrance examinationwidely adopted by Brazilian universities. This exam poses challenging tasks forLMs, since its questions may span into multiple fields of knowledge, requiringunderstanding of information from diverse domains. For instance, a question mayrequire comprehension of both statistics and biology to be solved. This workanalyzed responses generated by GPT-3.5 and GPT-4 models for questionspresented in the 2009-2017 exams, as well as for questions of the 2022 exam,which were made public after the training of the models was completed.Furthermore, different prompt strategies were tested, including the use ofChain-of-Thought (CoT) prompts to generate explanations for answers. On the2022 edition, the best-performing model, GPT-4 with CoT, achieved an accuracyof 87%, largely surpassing GPT-3.5 by 11 points. The code and data used onexperiments are available at https://github.com/piresramon/gpt-4-enem.

Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams