Automatic Metric

Automatic metrics aim to objectively assess the quality of outputs from natural language processing (NLP) and other AI systems, primarily by correlating with human judgments. Current research focuses on improving the accuracy and reliability of these metrics, particularly addressing their limitations in capturing nuanced aspects of quality, especially at higher performance levels, and developing metrics tailored to specific tasks (e.g., LaTeX formatting, video generation). This work is crucial for efficient and unbiased evaluation of NLP systems, enabling faster progress in model development and facilitating more rigorous comparisons across different approaches.