WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
Authors: name: Zhongyu Yang, name: Jun Chen, name: Dannong Xu, name: Junjie Fei, name: Xiaoqian Shen, name: Liangbing Zhao, name: Chun-Mei Feng, name: Mohamed Elhoseiny

Knowledge discovery and collection are intelligence-intensive tasks that
traditionally require significant human effort to ensure high-quality outputs.
Recent research has explored multi-agent frameworks for automating
Wikipedia-style article generation by retrieving and synthesizing information
from the internet. However, these methods primarily focus on text-only
generation, overlooking the importance of multimodal content in enhancing
informativeness and engagement. In this work, we introduce WikiAutoGen, a novel
system for automated multimodal Wikipedia-style article generation. Unlike
prior approaches, WikiAutoGen retrieves and integrates relevant images
alongside text, enriching both the depth and visual appeal of generated
content. To further improve factual accuracy and comprehensiveness, we propose
a multi-perspective self-reflection mechanism, which critically assesses
retrieved content from diverse viewpoints to enhance reliability, breadth, and
coherence, etc. Additionally, we introduce WikiSeek, a benchmark comprising
Wikipedia articles with topics paired with both textual and image-based
representations, designed to evaluate multimodal knowledge generation on more
challenging topics. Experimental results show that WikiAutoGen outperforms
previous methods by 8%-29% on our WikiSeek benchmark, producing more accurate,
coherent, and visually enriched Wikipedia-style articles. We show some of our
generated examples in https://wikiautogen.github.io/ .