NLG Metric

NLG metric research focuses on developing effective methods for automatically assessing the quality of generated text, moving beyond simple surface-level comparisons to capture meaning and context. Current efforts concentrate on improving the reliability and robustness of existing metrics, particularly addressing their limitations in handling nuanced aspects like dialect variation and factual accuracy, often employing large language models (LLMs) for evaluation or incorporating graph-based representations of meaning. These advancements are crucial for advancing NLG systems, enabling more objective and comprehensive evaluation, and ultimately leading to more effective and human-like text generation in various applications.