Multimodal Task

Multimodal tasks involve integrating information from multiple sources like text, images, and audio to perform complex reasoning and generation. Current research focuses on developing and evaluating large multimodal models (LLMs) using techniques like next-token prediction, prompt tuning, and mixture-of-experts architectures to improve efficiency and performance across diverse tasks, including visual question answering and image captioning. These advancements are significant for improving the capabilities of AI systems in various fields, particularly those requiring the interpretation and generation of multimodal data, such as healthcare and insurance. Addressing challenges like hallucination and improving the explainability of these models remains a key focus.