@inproceedings{xi-etal-2025-omnithink,
title = "{O}mni{T}hink: Expanding Knowledge Boundaries in Machine Writing through Thinking",
author = "Xi, Zekun and
Yin, Wenbiao and
Fang, Jizhan and
Wu, Jialong and
Fang, Runnan and
Jiang, Yong and
Xie, Pengjun and
Huang, Fei and
Chen, Huajun and
Zhang, Ningyu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.50/",
pages = "956--976",
ISBN = "979-8-89176-332-6",
abstract = "Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model{'}s predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, novelty, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, unoriginal, and repetitive outputs. To address these issues, we propose OmniThink, a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they slowly deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles."
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<abstract>Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model’s predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, novelty, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, unoriginal, and repetitive outputs. To address these issues, we propose OmniThink, a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they slowly deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles.</abstract>
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%0 Conference Proceedings
%T OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
%A Xi, Zekun
%A Yin, Wenbiao
%A Fang, Jizhan
%A Wu, Jialong
%A Fang, Runnan
%A Jiang, Yong
%A Xie, Pengjun
%A Huang, Fei
%A Chen, Huajun
%A Zhang, Ningyu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F xi-etal-2025-omnithink
%X Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model’s predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, novelty, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, unoriginal, and repetitive outputs. To address these issues, we propose OmniThink, a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they slowly deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles.
%U https://aclanthology.org/2025.emnlp-main.50/
%P 956-976
Markdown (Informal)
[OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking](https://aclanthology.org/2025.emnlp-main.50/) (Xi et al., EMNLP 2025)
ACL
- Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. 2025. OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 956–976, Suzhou, China. Association for Computational Linguistics.