Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams

28 Dec 2023 · 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.

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Adam • Attention Dropout • BPE • Cosine Annealing • Dense Connections • Dropout • Fixed Factorized Attention • GELU • GPT-3 • Layer Normalization • Linear Layer • Linear Warmup With Cosine Annealing • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Strided Attention • Weight Decay

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Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams

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