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Machine writing using large-scale language models often relies on retrieval-based generation, but the model's predefined scope limits the creation of rich content. Existing retrieval-based information lacks depth, novelty, and redundancy, resulting in poorly generated articles. In this paper, we propose OmniThink, a slow-thinking machine writing framework that mimics the human process of iterative expansion and reflection. The core idea of OmniThink is to simulate the cognitive behavior of a learner gradually deepening their knowledge of a topic. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluation and expert feedback highlight OmniThink's potential for solving the real-world problem of long-form article generation. The code is available at https://github.com/zjunlp/OmniThink .
We present OmniThink, a novel framework that addresses the problems of lack of depth, lack of novelty, and redundancy in existing search-based machine writing (T640_____).
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Suggesting the possibility of generating long-form articles with high knowledge density by mimicking human cognitive processes.
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Experimentally validated improvement of knowledge density while maintaining consistency and depth metrics.
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To identify its potential for solving real-world problems in the field of long-form article generation.
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Improving accessibility through open source code disclosure.
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Limitations:
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OmniThink's performance may be biased towards certain datasets or types of long articles.
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There may be limits to perfectly mimicking human thought processes.
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More diverse and extensive experiments and evaluations are needed.
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It is unlikely that the limitations of large-scale language models will be completely overcome.
