Model Synergy

Model synergy explores how combining different models or model components can achieve superior performance compared to using individual models alone. Current research focuses on leveraging this synergy in various applications, including improving large language models (LLMs) through modular designs and adaptive ensembling, enhancing the efficiency and accuracy of retrieval-augmented generation, and optimizing deep learning architectures for specific tasks like image processing and drug synergy prediction. This research is significant because it offers more efficient and effective solutions across diverse fields, ranging from robotics and autonomous systems to healthcare and materials science.