  The majority of automatic metrics for evaluating NLG systems are
reference-based. However, the challenge of collecting human annotation results
in a lack of reliable references in numerous application scenarios. Despite
recent advancements in reference-free metrics, it has not been well understood
when and where they can be used as an alternative to reference-based metrics.
In this study, by employing diverse analytical approaches, we comprehensively
assess the performance of both metrics across a wide range of NLG tasks,
encompassing eight datasets and eight evaluation models. Based on solid
experiments, the results show that reference-free metrics exhibit a higher
correlation with human judgment and greater sensitivity to deficiencies in
language quality. However, their effectiveness varies across tasks and is
influenced by the quality of candidate texts. Therefore, it's important to
assess the performance of reference-free metrics before applying them to a new
task, especially when inputs are in uncommon form or when the answer space is
highly variable. Our study can provide insight into the appropriate application
of automatic metrics and the impact of metric choice on evaluation performance.
