OmniThink：通过思维拓展机器写作中的知识边界

OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking

January 16, 2025

作者: Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Ningyu Zhang, Jiang Yong, Pengjun Xie, Fei Huang, Huajun Chen

cs.AI

摘要

使用大型语言模型进行机器写作通常依赖于检索增强生成。然而，这些方法仍然局限于模型预定义范围内，限制了生成具有丰富信息的内容。具体而言，普通检索到的信息往往缺乏深度、实用性，并且存在冗余，这会对生成的文章质量产生负面影响，导致表面化、重复和缺乏创意的输出。为了解决这些问题，我们提出了OmniThink，这是一个模拟迭代扩展和反思的类人机器写作框架。OmniThink背后的核心思想是模拟学习者逐渐加深对主题知识的过程。实验结果表明，OmniThink提高了生成文章的知识密度，同时不影响连贯性和深度等指标。人类评估和专家反馈进一步突显了OmniThink在长篇文章生成中解决现实挑战的潜力。

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, 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.