In the dynamic landscape of Natural Language Processing (NLP), the evaluation of Language Model (LM) performance stands as a pivotal aspect in gauging their efficacy across various downstream applications. Different applications demand distinct performance indicators aligned with their goals. In this article, we'll take a detailed look at various LLM evaluation metrics, exploring how they apply to real-world scenarios. From traditional summarization tasks to more nuanced contextual evaluations, we navigate through the evolving methodologies employed to assess the proficiency of Language Models, shedding light on their strengths, limitations, and practical implications in driving advancements in NLP research and applications. Below are some common Text Application tasks and corresponding evaluation metrics/frameworks.

1. Text Summarization

Text summarization is a natural language processing (NLP) task aimed at reducing/distilling the content of a given text document into a shorter version while retaining the most important information and the overall meaning of the original text. Text summarization can be performed using extractive or abstractive techniques. Some of the metrics/frameworks for evaluating such a system can be: