Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866914984922972160 |
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| author | Feng, Zhangyin Ma, Weitao Yu, Weijiang Huang, Lei Wang, Haotian Chen, Qianglong Peng, Weihua Feng, Xiaocheng Qin, Bing liu, Ting |
| author_facet | Feng, Zhangyin Ma, Weitao Yu, Weijiang Huang, Lei Wang, Haotian Chen, Qianglong Peng, Weihua Feng, Xiaocheng Qin, Bing liu, Ting |
| contents | Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursued two primary strategies, knowledge editing and retrieval augmentation, to enhance LLMs by incorporating external information from different aspects. Nevertheless, there is still a notable absence of a comprehensive survey. In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications. In addition, we conduct an in-depth analysis of different methods and point out potential research directions in the future. We hope this survey offers the community quick access and a comprehensive overview of this research area, with the intention of inspiring future research endeavors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_05876 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications Feng, Zhangyin Ma, Weitao Yu, Weijiang Huang, Lei Wang, Haotian Chen, Qianglong Peng, Weihua Feng, Xiaocheng Qin, Bing liu, Ting Computation and Language Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursued two primary strategies, knowledge editing and retrieval augmentation, to enhance LLMs by incorporating external information from different aspects. Nevertheless, there is still a notable absence of a comprehensive survey. In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications. In addition, we conduct an in-depth analysis of different methods and point out potential research directions in the future. We hope this survey offers the community quick access and a comprehensive overview of this research area, with the intention of inspiring future research endeavors. |
| title | Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.05876 |