Intermediate Prediction

Intermediate predictions, the outputs generated at various stages within a model's processing, are becoming a key focus in improving efficiency and understanding of complex machine learning models. Research currently explores using these intermediate predictions for tasks such as model explainability (e.g., attributing importance to input features), uncertainty quantification, and efficient inference through early-exit strategies. This work spans various architectures, including transformers and early-exit neural networks, and aims to enhance model performance, reduce computational costs, and provide valuable insights into model behavior. The resulting advancements have implications for diverse applications, from improving the accuracy and speed of speech recognition to enhancing the privacy and efficiency of large language model fine-tuning.