Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion

Mingyang Wang; Alisa Stoll; Lukas Lange; Heike Adel; Hinrich Schütze; Jannik Strötgen

doi:10.18653/v1/2025.l2m2-1.12

Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion

Mingyang Wang, Alisa Stoll, Lukas Lange, Heike Adel, Hinrich Schuetze, Jannik Strötgen

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Abstract

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.

Anthology ID:: 2025.l2m2-1.12
Volume:: Proceedings of the First Workshop on Large Language Model Memorization (L2M2)
Month:: August
Year:: 2025
Address:: Vienna, Austria
Editors:: Robin Jia, Eric Wallace, Yangsibo Huang, Tiago Pimentel, Pratyush Maini, Verna Dankers, Johnny Wei, Pietro Lesci
Venues:: L2M2 | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 150–168
Language:
URL:: https://aclanthology.org/2025.l2m2-1.12/
DOI:: 10.18653/v1/2025.l2m2-1.12
Bibkey:
Cite (ACL):: Mingyang Wang, Alisa Stoll, Lukas Lange, Heike Adel, Hinrich Schuetze, and Jannik Strötgen. 2025. Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion. In Proceedings of the First Workshop on Large Language Model Memorization (L2M2), pages 150–168, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):: Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion (Wang et al., L2M2 2025)
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PDF:: https://aclanthology.org/2025.l2m2-1.12.pdf

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@inproceedings{wang-etal-2025-bring,
    title = "Bring Your Own Knowledge: A Survey of Methods for {LLM} Knowledge Expansion",
    author = {Wang, Mingyang  and
      Stoll, Alisa  and
      Lange, Lukas  and
      Adel, Heike  and
      Schuetze, Hinrich  and
      Str{\"o}tgen, Jannik},
    editor = "Jia, Robin  and
      Wallace, Eric  and
      Huang, Yangsibo  and
      Pimentel, Tiago  and
      Maini, Pratyush  and
      Dankers, Verna  and
      Wei, Johnny  and
      Lesci, Pietro",
    booktitle = "Proceedings of the First Workshop on Large Language Model Memorization (L2M2)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.l2m2-1.12/",
    doi = "10.18653/v1/2025.l2m2-1.12",
    pages = "150--168",
    ISBN = "979-8-89176-278-7",
    abstract = "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."
}

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%0 Conference Proceedings
%T Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion
%A Wang, Mingyang
%A Stoll, Alisa
%A Lange, Lukas
%A Adel, Heike
%A Schuetze, Hinrich
%A Strötgen, Jannik
%Y Jia, Robin
%Y Wallace, Eric
%Y Huang, Yangsibo
%Y Pimentel, Tiago
%Y Maini, Pratyush
%Y Dankers, Verna
%Y Wei, Johnny
%Y Lesci, Pietro
%S Proceedings of the First Workshop on Large Language Model Memorization (L2M2)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-278-7
%F wang-etal-2025-bring
%X 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.
%R 10.18653/v1/2025.l2m2-1.12
%U https://aclanthology.org/2025.l2m2-1.12/
%U https://doi.org/10.18653/v1/2025.l2m2-1.12
%P 150-168

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Markdown (Informal)

[Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion](https://aclanthology.org/2025.l2m2-1.12/) (Wang et al., L2M2 2025)

Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion (Wang et al., L2M2 2025)

ACL

Mingyang Wang, Alisa Stoll, Lukas Lange, Heike Adel, Hinrich Schuetze, and Jannik Strötgen. 2025. Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion. In Proceedings of the First Workshop on Large Language Model Memorization (L2M2), pages 150–168, Vienna, Austria. Association for Computational Linguistics.