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

被引:1
|
作者
Shriram, Jaidev [1 ]
Sreekala, Sanjayan [1 ]
机构
[1] Univ Calif San Diego, La Jolla, CA 92093 USA
关键词
Large Language Models; Text-to-Image Generation; Information Extraction; Generative Art; Zines; Summarization;
D O I
10.1145/3586182.3625118
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
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页数:3
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