OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking

2025-01-16

AI Summary

To address shallow knowledge coverage, redundancy, and insufficient depth and originality in long-text generation by large language models, this paper proposes a human-like progressive knowledge deepening framework. Departing from static, single-shot retrieval in conventional RAG, our approach establishes a closed-loop “retrieve–generate–reflect” cycle, integrating cognitive modeling, knowledge distillation, and controllable rewriting to enable dynamic, multi-turn, reflection-driven knowledge expansion. Compared to existing methods, the framework significantly enhances knowledge density and logical depth of generated texts while preserving semantic coherence and expressive originality. Human evaluation and expert feedback from domain specialists confirm its substantial practical utility and scalability for long-form generation tasks—particularly in technical commentary and literature review writing.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Large Model Writing
• Information Depth
• Engagement Quality

Innovation

• OmniThink
• Deep Learning Imitation
• Enhanced Writing Quality