Model Failure

Model failure in machine learning, particularly concerning large language models (LLMs) and deep learning systems, focuses on identifying, understanding, and mitigating instances where models deviate from expected performance. Current research emphasizes developing methods for detecting systematic biases and failures, often leveraging techniques like prompt engineering, uncertainty quantification, and generative models to create targeted datasets for improving model robustness. This work is crucial for ensuring the reliability and fairness of AI systems across diverse applications, ranging from healthcare and education to safety-critical domains like aviation, where model failures can have significant consequences.