Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

• URL: http://arxiv.org/abs/2401.07103v2
• Date: Wed, 12 Jun 2024 08:31:58 GMT
• Title: Leveraging Large Language Models for NLG Evaluation: Advances and Challenges
• Authors: Zhen Li, Xiaohan Xu, Tao Shen, Can Xu, Jia-Chen Gu, Yuxuan Lai, Chongyang Tao, Shuai Ma,
• Abstract summary: Large Language Models (LLMs) have opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. We propose a coherent taxonomy for organizing existing LLM-based evaluation metrics, offering a structured framework to understand and compare these methods. By discussing unresolved challenges, including bias, robustness, domain-specificity, and unified evaluation, this paper seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.
• Score: 57.88520765782177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 paper 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 paper seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.

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