RevisEval: Improving LLM-as-a-Judge via Response-Adapted References
Authors: name: Qiyuan Zhang, name: Yufei Wang, name: Tiezheng YU, name: Yuxin Jiang, name: Chuhan Wu, name: Liangyou Li, name: Yasheng Wang, name: Xin Jiang, name: Lifeng Shang, name: Ruiming Tang, name: Fuyuan Lyu, name: Chen Ma

With significant efforts in recent studies, LLM-as-a-Judge has become a
cost-effective alternative to human evaluation for assessing the text
generation quality in a wide range of tasks. However, there still remains a
reliability gap between LLM-as-a-Judge and human evaluation. One important
reason is the lack of guided oracles in the evaluation process. Motivated by
the role of reference pervasively used in classic text evaluation, we introduce
RevisEval, a novel text generation evaluation paradigm via the response-adapted
references. RevisEval is driven by the key observation that an ideal reference
should maintain the necessary relevance to the response to be evaluated.
Specifically, RevisEval leverages the text revision capabilities of large
language models (LLMs) to adaptively revise the response, then treat the
revised text as the reference (response-adapted reference) for the subsequent
evaluation. Extensive experiments demonstrate that RevisEval outperforms
traditional reference-free and reference-based evaluation paradigms that use
LLM-as-a-Judge across NLG tasks and open-ended instruction-following tasks.
More importantly, our response-adapted references can further boost the
classical text metrics, e.g., BLEU and BERTScore, compared to traditional
references and even rival the LLM-as-a-Judge. A detailed analysis is also
conducted to confirm RevisEval's effectiveness in bias reduction, the impact of
inference cost, and reference relevance.