Evaluating the Performance of Large Language Models for Spanish Language
  in Undergraduate Admissions Exams
Sabino Miranda Obdulia Pichardo-Lagunas Bella Martínez-Seis Pierre Baldi
This study evaluates the performance of large language models, specifically
GPT-3.5 and BARD (supported by Gemini Pro model), in undergraduate admissions
exams proposed by the National Polytechnic Institute in Mexico. The exams cover
Engineering/Mathematical and Physical Sciences, Biological and Medical
Sciences, and Social and Administrative Sciences. Both models demonstrated
proficiency, exceeding the minimum acceptance scores for respective academic
programs to up to 75% for some academic programs. GPT-3.5 outperformed BARD in
Mathematics and Physics, while BARD performed better in History and questions
related to factual information. Overall, GPT-3.5 marginally surpassed BARD with
scores of 60.94% and 60.42%, respectively.