In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. This survey aims to provide a thorough overview of leveraging LLMs for NLG evaluation, a burgeoning area that lacks a systematic analysis. We propose a coherent taxonomy for organizing existing LLM-based evaluation metrics, offering a structured framework to understand and compare these methods. Our detailed exploration includes critically assessing various LLM-based methodologies, as well as comparing their strengths and limitations in evaluating NLG outputs. By discussing unresolved challenges, including bias, robustness, domain-specificity, and unified evaluation, this survey seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.

自然语言生成（NLG）评估中引入大型语言模型（LLM）为评估生成内容质量提供了新的途径，本文提供了对利用LLM进行NLG评估的全面概述，包括组织现有基于LLM的评估指标的一致性分类法、批判性评估不同的LLM方法以及比较其在评估NLG输出中的优势和局限性，讨论未解决的挑战，并推动更公平、更先进的NLG评估技术。

利用大型语言模型进行自然语言生成评估：综述