Benchmarking open-source large language models on Portuguese Revalida multiple-choice questions - João Victor Bruneti Severino; Pedro Angelo Basei de Paula; Matheus Nespolo Berger; Filipe Silveira Loures; Solano Amadori Todeschini; Eduardo Augusto Roeder; Maria Han Veiga; Murilo Guedes; Gustavo Lenci Marques (BMJ Health & Care Informatics)
Objective The study aimed to evaluate the top large language models (LLMs) in validated medical knowledge tests in Portuguese.Methods This study compared 31 LLMs in the context of solving the national Brazilian medical examination test. The research compared the performance of 23 open-source and 8 proprietary models across 399 multiple-choice questions.Results Among the smaller models, Llama 3 8B exhibited the highest success rate, achieving 53.9%, while the medium-sized model Mixtral 8×7B attained a success rate of 63.7%. Conversely, larger models like Llama 3 70B achieved a success rate of 77.5%. Among the proprietary models, GPT-4o and Claude Opus demonstrated superior accuracy, scoring 86.8% and 83.8%, respectively.Conclusions 10 out of the 31 LLMs attained better than human level of performance in the Revalida benchmark, with 9 failing to provide coherent answers to the task. Larger models exhibited superior performance overall. However, certain medium-sized LLMs surpassed the performance of some of the larger LLMs.