Auxiliary Model

Auxiliary models are secondary models used to enhance the performance or interpretability of primary machine learning models. Current research focuses on diverse applications, including improving model stability and consistency in real-world settings, accelerating inference speed through techniques like speculative decoding, and enhancing multimodal representation learning via knowledge distillation. These advancements contribute to improved model accuracy, efficiency, and explainability across various domains, impacting fields such as medical image analysis, natural language processing, and causal inference.