  N-gram matching-based evaluation metrics, such as BLEU and chrF, are widely
utilized across a range of natural language generation (NLG) tasks. However,
recent studies have revealed a weak correlation between these matching-based
metrics and human evaluations, especially when compared with neural-based
metrics like BLEURT. In this paper, we conjecture that the performance
bottleneck in matching-based metrics may be caused by the limited diversity of
references. To address this issue, we propose to utilize \textit{multiple
references} to enhance the consistency between these metrics and human
evaluations. Within the WMT Metrics benchmarks, we observe that the
multi-references F200spBLEU surpasses the conventional single-reference one by
an accuracy improvement of 7.2\%. Remarkably, it also exceeds the neural-based
BERTscore by an accuracy enhancement of 3.9\%. Moreover, we observe that the
data leakage issue in large language models (LLMs) can be mitigated to a large
extent by our multi-reference metric. We release the code and data at
\url{https://github.com/SefaZeng/LLM-Ref}
