OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917093354504192 |
|---|---|
| author | Xi, Zekun Yin, Wenbiao Fang, Jizhan Wu, Jialong Fang, Runnan Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhang, Ningyu |
| author_facet | Xi, Zekun Yin, Wenbiao Fang, Jizhan Wu, Jialong Fang, Runnan Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhang, Ningyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking Xi, Zekun Yin, Wenbiao Fang, Jizhan Wu, Jialong Fang, Runnan Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Zhang, Ningyu Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval Machine Learning 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. |
| title | OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2501.09751 |