WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
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/ .
67e3deabf2cb5de878fbf557
Zhongyu Yang
claimed_verified
2025-03-26T20:44:00.382Z
654f99f74c8874c64d4e5664
/avatars/e9da0d688f91ae49db91d0ebebb3782a.svg
杨忠裕
user
yzzyu
67e3deabf2cb5de878fbf558
Jun Chen
67e3deabf2cb5de878fbf559
Dannong Xu
claimed_verified
2025-03-27T09:04:55.483Z
66c0e1cdb83a7e94d5969f24
/avatars/ee990b2e5fd4af08cd3691733e3a4e7c.svg
DanielX
user
DanielXu0208
67e3deabf2cb5de878fbf55a
Junjie Fei
67e3deabf2cb5de878fbf55b
Xiaoqian Shen
67e3deabf2cb5de878fbf55c
Liangbing Zhao
67e3deabf2cb5de878fbf55d
Chun-Mei Feng
67e3deabf2cb5de878fbf55e
Mohamed Elhoseiny
WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
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/ .
WikiAutoGen automates the creation of multimodal Wikipedia-style articles by integrating text and images, using a multi-perspective self-reflection mechanism to improve accuracy and coherence.
WikiAutoGen multimodal content multi-perspective self-reflection WikiSeek factual accuracy comprehensiveness