OmniThink: Expanding Knowledge Boundaries in Machine Writing through
  Thinking
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.
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Zekun Xi
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2025-01-17T10:29:57.432Z
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Xi Ze Kun
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Wenbiao Yin
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Jizhan Fang
admin_assigned
2025-01-17T10:30:13.133Z
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Jizhan Fang
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JizhanFang
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Jialong Wu
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2025-01-17T10:31:40.494Z
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Jialong Wu
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Runnan Fang
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2025-01-17T10:31:49.555Z
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Runnan Fang
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Runnaning
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Ningyu Zhang
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2025-01-17T10:25:07.769Z
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Ningyu Zhang
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Ningyu
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Jiang Yong
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Pengjun Xie
admin_assigned
2025-01-17T10:32:07.000Z
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pengjun xie
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Fei Huang
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2025-01-17T10:32:22.107Z
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Fei Huang
user
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Huajun Chen
admin_assigned
2025-01-17T10:32:36.118Z
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Huajun Chen
user
huajunsir
OmniThink: Expanding Knowledge Boundaries in Machine Writing through
  Thinking
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.
OmniThink is a machine writing framework that simulates human iterative learning to enhance the quality and depth of generated long-form articles.
retrieval-augmented generation predefined scope vanilla-retrieved information iterative expansion knowledge density coherence depth