Text Quality

Assessing text quality is crucial for advancing natural language processing, particularly with the rise of large language models (LLMs). Current research focuses on developing more robust and human-aligned evaluation metrics, often leveraging LLMs themselves or ensemble methods combining different language models and n-gram approaches to better capture nuanced aspects of text quality like coherence, fluency, and faithfulness. These improvements are vital for enhancing the reliability of LLM-generated content and for optimizing the training of future models by enabling more effective filtering of training data and improving the efficiency of the training process.