NLG Evaluation

Evaluating the quality of Natural Language Generation (NLG) systems is a crucial but challenging task. Current research focuses on improving the efficiency and reliability of evaluation, exploring methods like active learning to reduce the need for expensive human judgments and leveraging large language models (LLMs) as automated evaluators, while carefully addressing their limitations and potential biases. These efforts aim to create more robust and cost-effective evaluation frameworks, ultimately leading to better NLG systems and facilitating more informed comparisons across different models and applications. A key challenge remains aligning automated evaluations with human preferences, particularly for complex tasks like story generation.