Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913695844532224 |
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| author | Wang, Mingyang Stoll, Alisa Lange, Lukas Adel, Heike Schütze, Hinrich Strötgen, Jannik |
| author_facet | Wang, Mingyang Stoll, Alisa Lange, Lukas Adel, Heike Schütze, Hinrich Strötgen, Jannik |
| contents | Adapting large language models (LLMs) to new and diverse knowledge is essential for their lasting effectiveness in real-world applications. This survey provides an overview of state-of-the-art methods for expanding the knowledge of LLMs, focusing on integrating various knowledge types, including factual information, domain expertise, language proficiency, and user preferences. We explore techniques, such as continual learning, model editing, and retrieval-based explicit adaptation, while discussing challenges like knowledge consistency and scalability. Designed as a guide for researchers and practitioners, this survey sheds light on opportunities for advancing LLMs as adaptable and robust knowledge systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12598 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion Wang, Mingyang Stoll, Alisa Lange, Lukas Adel, Heike Schütze, Hinrich Strötgen, Jannik Computation and Language Adapting large language models (LLMs) to new and diverse knowledge is essential for their lasting effectiveness in real-world applications. This survey provides an overview of state-of-the-art methods for expanding the knowledge of LLMs, focusing on integrating various knowledge types, including factual information, domain expertise, language proficiency, and user preferences. We explore techniques, such as continual learning, model editing, and retrieval-based explicit adaptation, while discussing challenges like knowledge consistency and scalability. Designed as a guide for researchers and practitioners, this survey sheds light on opportunities for advancing LLMs as adaptable and robust knowledge systems. |
| title | Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.12598 |