With the growing prevalence of generative AI, an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. To further enhance real-world applicability, we extend the framework to estimate the minimal necessary human contribution for any text without requiring human input and validate its effectiveness. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.

“近年来,运用复杂网络方法进行语言学研究已成为数字人文研究的一条新路径。本文基于214篇日本汉语学习者的书面作文,构建了6个不同能力水平的汉语中介语词同现网络,并探讨了这些网络的结构特性及其动态演变过程。研究结果显示,所有的汉语中介语词同现网络均呈现出小世界属性、无标度属性、异配性和层级结构等复杂网络的特性。这些特性揭示了汉语学习者在词汇使用方面的特定模式:低水平学习者更倾向于将低频词汇与高频词汇进行连接,这可能与学习者减轻认知负荷的习得模式有关;学习者语言水平的提升,中介语网络参数会逐渐向母语者靠拢,但是无法达到母语者的水平;此外,本研究还观察到,语言错误会对中介语网络结构产生影响,引起网络结构的变异。”