We evaluated the performance of Large Multimodal Models (LMMs) in financial reasoning using the FinMME dataset. This analysis compared LMMs with traditional large language models (LLMs), highlighting differences in their architectural designs, including text and image encoders. Models like Llama 4 Maverick excel in multimodal tasks due to advanced architecture, while smaller models often struggle due to limited parameter counts. Effective training data coverage is crucial, as models trained on diverse multimodal datasets outperform those relying on general-purpose data. Fine-tuning for cross-modal reasoning enhances performance, especially in handling complex visual elements like charts and diagrams. Moreover, LMMs, capable of integrating text, images, audio, and video, represent progress toward artificial general intelligence. However, computational demands and integration challenges persist. As AI evolves, selecting the right model involves balancing performance, cost, and specific application needs, fostering advancements in versatile AI agents.
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