Mixture of Knowledge Minigraph Agents for Literature Review Generation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916594561581056 |
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| author | Zhang, Zhi Liu, Yan Zhong, Sheng-hua Chen, Gong Yang, Yu Cao, Jiannong |
| author_facet | Zhang, Zhi Liu, Yan Zhong, Sheng-hua Chen, Gong Yang, Yu Cao, Jiannong |
| contents | Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06159 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mixture of Knowledge Minigraph Agents for Literature Review Generation Zhang, Zhi Liu, Yan Zhong, Sheng-hua Chen, Gong Yang, Yu Cao, Jiannong Computation and Language Computational Engineering, Finance, and Science Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. |
| title | Mixture of Knowledge Minigraph Agents for Literature Review Generation |
| topic | Computation and Language Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2411.06159 |