Using Retrieval-Augmented Generation to improve Performance of Large Language Models on the Brazilian University Admission Exam

Abstract

The Brazilian University Admission Exam (ENEM) presents a unique challenge for artificial intelligence. It requires deep mastering of knowledge from diverse fields. Recently, Language Models (LMs) with growing numbers of parameters have established the state-of-the-art performance on ENEM. However, techniques like Retrieval-Augmented Generation (RAG) can help further improvements, by exploiting trustfull knowledge bases to enhance contexts and reduce non-factual responses. This study investigates how RAG can improve LMs’ performance on ENEM. The experiments reported in this article use up-to-date versions of four popular LMs, with and without RAG, on text-only and multi-modal data. The results reveal consistent gains using RAG with both kinds of data, across diverse fields, demonstrating the potential of RAG to improve LMs’ performance on tasks requiring multidisciplinary knowledge

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