G Eval

G-Eval, and related evaluation frameworks, address the critical need for robust and reliable methods to assess the performance of large language models (LLMs). Current research focuses on developing comprehensive benchmarks that evaluate LLMs across diverse tasks and domains, including safety, mathematical reasoning, and multilingual capabilities, often employing LLMs themselves as evaluators or incorporating hierarchical criteria decomposition. These advancements are crucial for improving LLM development, fostering fairer comparisons between models, and ensuring the responsible deployment of these powerful technologies in various applications.