Measuring the distance between machine-produced and human language is
acritical open problem. Inspired by empirical findings from psycholinguistics
on theperiodicity of entropy in language, we propose FACE, a set of metrics
based onFourier Analysis of the estimated Cross-Entropy of language, for
measuring thesimilarity between model-generated and human-written languages.
Based on anopen-ended generation task and the experimental data from previous
studies, weind that FACE can effectively identify the human-model gap, scales
with modelsize, reflects the outcomes of different sampling methods for
decoding, correlateswell with other evaluation metrics and with human judgment
scores. FACE iscomputationally efficient and provides intuitive
interpretations.

通过实验结果，本文提出了一种计算机生成语言与人类自然语言之间相似性的新方法 FACE，该方法基于熵的周期性实证研究的结果，采用傅立叶分析和交叉熵等指标度量模型生成的语言与人类自然语言之间的相似性，并在计算效率和可解释性方面都表现优异。