OmniThink: 通过思考扩展机器写作的知识边界
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
AI总结：
OmniThink通过模拟人类思考过程，提升机器写作的知识密度和原创性，解决传统方法在生成长文时的不足。
摘要：
大型语言模型的机器写作常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的范围，限制了生成丰富信息内容的能力。具体来说，常规的检索信息往往缺乏深度、新颖性和重复性，这会负面影响生成文章的质量，导致浅显、不原创和重复的输出。为了解决这些问题，我们提出了OmniThink，一种慢思考的机器写作框架，模拟人类迭代扩展和反思的过程。OmniThink的核心思想是模拟学习者在逐步深入主题知识时的认知行为。实验结果表明，OmniThink在不牺牲连贯性和深度等指标的情况下提高了生成文章的知识密度。人类评估和专家反馈进一步突显了OmniThink在长文生成中解决现实挑战的潜力。代码可在https://github.com/zjunlp/OmniThink上获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
