NLP Metric

NLP metrics are quantitative measures used to evaluate the performance of natural language processing (NLP) models, primarily aiming to objectively assess model accuracy and alignment with human judgment. Current research focuses on developing more robust metrics that better correlate with human evaluation, particularly for complex tasks like text summarization and chatbot evaluation, often comparing traditional metrics (e.g., BLEU, ROUGE) against newer approaches leveraging large language models (LLMs) and incorporating human feedback. Improved NLP metrics are crucial for advancing NLP research by enabling more reliable model comparisons, facilitating the development of more effective models, and ultimately improving the trustworthiness and real-world applicability of NLP technologies across diverse domains.