Text Diversity

Text diversity, encompassing the richness and variety of language in generated text, is a crucial area of research in natural language processing. Current efforts focus on developing metrics to quantify text diversity, investigating how model architectures and training data influence this diversity, and exploring methods to improve it, such as incorporating diverse training data and modifying model training objectives. These advancements are vital for enhancing the quality and reliability of large language models, mitigating issues like repetitive outputs and improving their applicability across diverse downstream tasks.