  In the study, we empirically compare the two recently proposed decoding
methods, i.e. Contrastive Search (CS) and Contrastive Decoding (CD), for
open-ended text generation. The automatic evaluation results suggest that,
while CS performs worse than CD on the MAUVE metric, it substantially surpasses
CD on the diversity and coherence metrics. More notably, extensive human
evaluations across three different domains demonstrate that human annotators
are universally more in favor of CS over CD with substantial margins.
  The contradicted results between MAUVE and human evaluations reveal that
MAUVE does not accurately reflect human preferences. Therefore, we call upon
the research community to develop better evaluation metrics for open-ended text
generation. To ensure the reproducibility of our work, we have open-sourced all
our code, evaluation results, as well as human annotations at
https://github.com/yxuansu/Contrastive_Search_versus_Contrastive_Decoding.
