ZINify: Transforming Research Papers into Engaging Zines with Large Language Models

University of California, San Diego (* equal contribution)
🏅 Honourable Mention (People’s Choice) - UIST Student Innovation Contest ‘23
Research papers are a vital building block for scientific discussion. While these papers follow effective structures for the relevant community, they are unable to cater to novice readers and express otherwise creative ideas in creative mediums. To this end, we propose ZINify, the first approach to automatically transform research papers into engaging zines using large language models (LLM) and text-to-image generators. Following zine’s long history of supporting independent, creative expression, we propose a technique that can work with authors to build more engaging, marketable, and unconventional content that is based on their research. We believe that our work will help make research more engaging and accessible to all while helping papers stand out in crowded online venues.


    author = {Shriram, Jaidev and Pradeep Kumar Sreekala, Sanjayan},
    title = {ZINify: Transforming Research Papers into Engaging Zines with Large Language Models},
    year = {2023},
    isbn = {9798400700965},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3586182.3625118},
    doi = {10.1145/3586182.3625118},
    booktitle = {Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology},
    articleno = {117},
    numpages = {3},
    location = {San Francisco, CA, USA},
    series = {UIST '23 Adjunct}