让AI写作像人一样反复思考，生成更深入、新颖的长文。
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
- 通过迭代反思模拟人类学习过程，扩展知识边界
- 生成内容知识密度显著提升，且保持流畅与深度
- 适合需要高质量长文本的学术写作、新闻创作等场景
大语言模型在机器写作中常依赖检索增强生成，但现有方法受限于预设知识范围，导致生成内容深度不足、缺乏新意且冗余严重，影响文章质量。为此，我们提出OmniThink——一种类人慢思考写作框架，模仿学习者逐步深化对主题的理解过程。该框架通过多轮迭代扩展与反思机制，主动突破原始知识边界。实验表明，OmniThink在不降低连贯性与深度的前提下，显著提升了生成文章的知识密度。人工评估与专家反馈进一步验证其在长文本生成中的实际应用潜力。代码已开源：https://github.com/zjunlp/OmniThink。
原文摘要 · Abstract (English)
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. Code is available at https://github.com/zjunlp/OmniThink.
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