Multimodal Integration in Large Language Models: A Case Study with Mistral LLM

Abstract

This work presents significant advancements in the multimodal capabilities of the Mistral 8x7B model, a large language model designed with eight experts of seven billion parameters each. We introduce comprehensive modifications to its architecture, data fusion techniques, and training procedures, aimed at improving the integration and processing of text, image, and audio data. Our experimental results demonstrate that these enhancements lead to superior performance across multiple modalities when compared to existing benchmarks. The improved model showcases enhanced accuracy, F1 scores, and a multimodal integration index, confirming its ability to offer more coherent and contextually appropriate outputs. This research not only sets new performance benchmarks for multimodal large language models but also opens up further avenues for applying such models in real-world, diverse, and dynamic environments.