Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models

Retrieval-Augmented Generation (RAG) has proven its effectiveness in alleviating hallucinations for Large Language Models (LLMs).However, existing automated evaluation metrics cannot fairly evaluate the outputs generated by RAG models during training and evaluation.LLM-based judgment models provide the potential to produce high-quality judgments, but they are highly sensitive to evaluation prompts, leading to inconsistencies when judging the output of RAG models.This paper introduces the Judge-Consistency (ConsJudge) method, which aims to enhance LLMs to generate more accurate evaluations for RAG models.Specifically, ConsJudge prompts LLMs to generate different judgments based on various combinations of judgment dimensions, utilizes the judge-consistency to evaluate these judgments, and selects the chosen and rejected judgments for DPO training.Our experiments show that ConsJudge can effectively provide more accurate judgments for optimizing RAG models across various RAG models and datasets.Further analysis reveals that judgments generated by ConsJudge have a high agreement with the superior LLM.All codes are available at https://github.com/OpenBMB/ConsJudge.Case 1: What are the virulence factors of anthrax?Ground Truth: Bacillus anthracis.Choice A: The virulence factors of anthrax are a group of proteins produced by the Bacillus anthracis bacterium that contribute to its ability to cause disease in humans and animals.Choice B: anthracis.Choice C: lethal factor, edema factor and antiphagocytic factor.Choice D: lethal factor,antiphagocytic factor and other factors.Raw Metric (ROUGE-L): Choice B(0.667) > Choice A(0.143) > Choice C(0) = Choice D(0) Zero-Shot: ...choice C and D are more complete as they specifically mention the names of the virulence factor... Best answer :C.SFT:... from the perspective of