Model Output

Model output analysis is a rapidly evolving field focused on understanding, interpreting, and improving the predictions generated by complex models, particularly in machine learning. Current research emphasizes interactive visualization techniques, often employing neural networks (like variational autoencoders and transformers) to enhance interpretability and facilitate model editing. This work is crucial for building trust in model predictions across diverse applications, from optimizing industrial processes (e.g., tool wear prediction) to improving the performance of large language models through feedback mechanisms and prompt engineering. The development of efficient methods for comparing and analyzing complex model outputs is also a key area of investigation.