OmniThink：透過思考擴展機器寫作中的知識邊界

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

大型語言模型的機器寫作通常依賴檢索增強生成。然而，這些方法仍受限於模型預定義範圍內，限制了生成豐富信息內容的能力。具體而言，普通檢索的信息往往缺乏深度、實用性，並且存在冗余，這對生成的文章質量產生負面影響，導致表面、重複和不具獨創性的輸出。為解決這些問題，我們提出了 OmniThink，一個模擬人類迭代擴展和反思過程的機器寫作框架。OmniThink 的核心理念是模擬學習者在逐步加深對主題知識的過程中的認知行為。實驗結果表明，OmniThink 提高了生成文章的知識密度，同時不損害連貫性和深度等指標。人工評估和專家反饋進一步突顯了 OmniThink 在長文生成中應對現實挑戰的潛力。

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.