Model Evaluation

Model evaluation in machine learning, particularly for large language models (LLMs), aims to accurately assess model performance and identify limitations. Current research emphasizes moving beyond simple accuracy metrics to incorporate probabilistic evaluations of output distributions, addressing issues like catastrophic forgetting and the impact of hard samples and data imbalances. This focus on more robust and nuanced evaluation is crucial for ensuring reliable model deployment across diverse applications, from healthcare to cybersecurity, and for fostering more rigorous and reproducible research practices within the broader AI community.