OmniThink: Expanding Knowledge Boundaries in Machine Writing through
  Thinking
Authors: name: Zekun Xi, name: Wenbiao Yin, name: Jizhan Fang, name: Jialong Wu, name: Runnan Fang, name: Ningyu Zhang, name: Jiang Yong, name: Pengjun Xie, name: Fei Huang, name: Huajun Chen

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, utility, and suffers from redundancy, which negatively
impacts the quality of generated articles, leading to shallow, repetitive, and
unoriginal outputs. To address these issues, we propose OmniThink, a 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 progressively 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.