CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable
  Evaluation of Large Language Model Generation
Pei Ke Bosi Wen Zhuoer Feng Xiao Liu Xuanyu Lei Jiale Cheng Shengyuan Wang Aohan Zeng Yuxiao Dong Hongning Wang Jie Tang Minlie Huang
Since the natural language processing (NLP) community started to make large
language models (LLMs), such as GPT-4, act as a critic to evaluate the quality
of generated texts, most of them only train a critique generation model of a
specific scale on specific datasets. We argue that a comprehensive
investigation on the key factor of LLM-based evaluation models, such as scaling
properties, is lacking, so that it is still inconclusive whether these models
have potential to replace GPT-4's evaluation in practical scenarios. In this
paper, we propose a new critique generation model called CritiqueLLM, which
includes a dialogue-based prompting method for high-quality referenced /
reference-free evaluation data. Experimental results show that our model can
achieve comparable evaluation performance to GPT-4 especially in system-level
correlations, and even outperform GPT-4 in 3 out of 8 tasks in a challenging
reference-free setting. We conduct detailed analysis to show promising scaling
properties of our model in the quality of generated critiques. We also
demonstrate that our generated critiques can act as scalable feedback to
directly improve the generation quality of LLMs.