FACE：基于交叉熵傅里叶分析的自然语言生成评估
FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy
AI总结：
本文提出FACE指标，基于语言交叉熵的傅里叶分析来衡量模型生成与人类语言的相似度，实验证明其能有效识别模型差距、随规模扩展且与人类判断高度相关。
摘要：
衡量机器生成与人类语言间的距离是一个关键的开放问题。受心理语言学关于语言熵周期性经验发现的启发，我们提出了FACE，一组基于语言估计交叉熵傅里叶分析的指标，用于测量模型生成与人类书写语言间的相似度。基于开放式生成任务及先前研究的实验数据，我们发现FACE能有效识别人类与模型间的差距，随模型规模扩展，反映解码中不同采样方法的结果，并与其他评估指标及人类判断分数良好相关。
英文摘要：
Measuring the distance between machine-produced and human language is a critical open problem. Inspired by empirical findings from psycholinguistics on the periodicity of entropy in language, we propose FACE, a set of metrics based on Fourier Analysis of the estimated Cross-Entropy of language, for measuring the similarity between model-generated and human-written languages. Based on an open-ended generation task and the experimental data from previous studies, we find that FACE can effectively identify the human-model gap, scales with model size, reflects the outcomes of different sampling methods for decoding, correlates well with other evaluation metrics and with human judgment scores.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
