The paper "Measuring Human Contribution in AI-Assisted Content Generation" addresses the challenge of determining how much human input influences content produced with generative AI. It proposes an information-theoretic framework that calculates the mutual information between human input and AI output relative to the self-information of the output, thereby quantifying the proportion of human contribution. Experiments across various creative domains show that this measure can distinguish different levels of human involvement in AI-assisted works.
By Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang, Ryan Whalen, Junming Huang, Qian Ding, Yu Xie, Xing Xie, Fangzhao Wu
arXiv:2510.08831v2 Announce Type: replace
Abstract: As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standar...
By Wouter Haverals, Meredith Martin
arXiv:2603.15034v2 Announce Type: replace-cross
Abstract: This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts. E...
By Adam Skurla, Dominik Macko, Jakub Simko
The paper introduces greCAPTCHA, a proctored assessment designed to gauge authors’ understanding of their own research manuscripts by measuring their capacity to verify content. It generates questions at multiple levels of comprehension and produces an evaluative report based on responses. A prototype study with 31 researchers showed that automated scores could predict authorship with an AUC of 0.90, and participants reported positive experiences and constructive feedback for future deployment.
By Justin Payan, B\'alint Gyevn\'ar, Atoosa Kasirzadeh, Nihar B. Shah
arXiv:2606. 00016v1 Announce Type: cross Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals.
By Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer, Salima Lamsiyah
arXiv:2606. 10794v1 Announce Type: new Abstract: As agentic applications increasingly route user tasks through official and third-party LLM APIs, provenance becomes an operational question: which model generated a given black-box response?
By Jiaxu Liu, Sunnan Mu, Dong Huang, Liuyin Wang, Jing Shao, Jie Zhang