Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion (2025)

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.

## introduction as large language models (llms) are increasingly deployed in real-world applications, their ability to adapt to evolving knowledge becomes crucial for maintaining relevance and accuracy. however, llms are typically trained once and thus only have knowledge up to a certain cutoff date, limiting their ability to stay updated with new information. this survey provides a comprehensive overview of methods that enable llms to incorporate various types of new knowledge, including factual, domainspecific, language, and user preference knowledge. we survey adaptation strategies, including continual learning, model editing, and retrieval-based approaches, and aim at providing guidelines for researchers and practitioners. to remain effective, llms require updates across multiple dimensions. factual knowledge consists of general truths and real-time information, while domain knowledge pertains to specialized fields, such as medicine or law. language knowledge enhances multilingual capabilities, and after placing our work into context (section 2) and defining knowledge types covered in this paper (section 3), we provide an overview of different knowledge expansion methods as detailed in figure 1 . this work thus surveys diverse research efforts and may serve as a guide for researchers and practitioners aiming to develop and apply adaptable and robust llms. we highlight research opportunities and provide insights into optimizing adaptation techniques for various real-world applications. ## related surveys the main goal of our work is to provide researchers and practitioners a broad overview of various types of methods to adapt llms to diverse types of new knowledge. in this section, we explain how other more specialized surveys relate to our paper. to the best of our knowledge, there is limited prior work that specifically focuses on continuous knowledge expansion for llms. closest to our work, @xcite describe temporal factual knowledge updates, while we take a broader perspective by examining methods for adapting llms to unseen domain knowledge, expanding language coverage, and incorporating user preferences. @xcite and @xcite provide overviews of knowledge editing methodologies, categorizing approaches of knowledge editing. similarly, @xcite , @xcite and @xcite offer a comprehensive overview of continual learning. in contrast, our survey shifts the focus towards a task-oriented perspective on knowledge expansion, detailing how various types of knowledge -including factual, domainspecific, language, and user preference knowledge -can be seamlessly integrated to ensure llms remain relevant and effective. ## knowledge types integrating diverse types of knowledge into llms is essential to enhance their versatility and effec-tiveness. depending on the use case, the type of knowledge that an llm shall be adapted to, might differ. in this paper, we distinguish four key types of knowledge, which cover a broad range of use cases of researchers and practitioners: factual, domain, language, and preference knowledge. in the next sections, we survey knowledge expansion methods and explain for which of these four knowledge types they are suitable. ## continual learning continual learning (cl) is a machine learning paradigm that mimics the human ability to continuously learn and accumulate knowledge without forgetting previously acquired information @xcite . in the context of knowledge expansion, cl allows llms to integrate new corpora and incrementally update the knowledge stored in their parameters. this ensures that llms remain adaptable, relevant, and effective as facts, domains, languages, and user preferences evolve over time. in the era of llms, the training of language models usually includes multiple stages: pretraining, instruction tuning, preference alignment, and potentially fine-tuning on a downstream task @xcite . depending on the stage, continual learning can be categorized into continual pretraining, continual instruction tuning, continual preference alignment, and continual end-task learning @xcite . for knowledge expansion, the focus lies on continual pretraining (cpt) and continual preference alignment (cpa). in contrast, continual instruction tuning and continual end-task learning primarily aim to sequentially fine-tune pretrained llms for acquiring new skills and solving new tasks, which fall outside the scope of this survey. in the following sections, we review existing studies that leverage continual pretraining for updating facts, adapting domains, and expanding languages, and continual alignment for updating user preferences. ## model editing model editing offers a controllable and efficient solution to update factual knowledge and user preferences in llms. introduced by @xcite , @xcite and @xcite , it aims at modifying the model's predictions for specific inputs without affecting unrelated ones. @xcite and @xcite define four key evaluation metrics for model editing: (1) reliability, ensuring the edited model produces the target prediction for the target input; (2) generalization, requiring the edited knowledge to apply to all in-scope inputs -inputs that are directly related to the target input, including rephrasings and semantically similar variations; (3) locality, preserving original outputs for unrelated out-of-scope inputs; and (4) portability, extending the generalization metric by assessing how well updated knowledge transfers to complex rephrasings, reasoning chains, and related facts. while recent works @xcite @xcite use model editing and knowledge editing interchangeably for updating factual knowledge, we distinguish between them: model editing is a subset of knowledge editing that modifies model parameters, whereas retrieval-based methods update knowledge dynamically without altering the model's parameters (see section 6). ## retrieval-based methods continual learning and model editing modify a model's parameters to update its internal knowledge, making them implicit knowledge expansion methods @xcite . in contrast, retrievalbased methods @xcite explicitly integrate external knowledge, allowing models to overwrite outdated or undesired information without parameter modifications. these methods leverage external sources, such as databases, off-the-shelf retriever systems, or the internet, and thus provide up-to-date or domain-specific knowledge @xcite , making them effective for factual updates and domain adaptation. ## hybrid methods beyond the traditional paradigms of continual learning, model editing, and retrieval, recent research has introduced hybrid methods that integrate elements from multiple approaches. these include continual model editing, which blends continual learning with model editing; retrieval-augmented knowledge editing, as discussed in section 6.1; and retrieval-augmented continual editing, which combines all three for knowledge updates. below, we highlight representative hybrid methods for expanding and updating llm knowledge. transformer-patcher @xcite , grace @xcite , and dafnet @xcite follow a correction-based continual editing paradigm, using lightweight, localized mechanisms to incrementally fix errors while preserving the pretrained model. transformer-patcher adds a dedicated neuron per error in the final ffn layer, activated only when needed. grace uses a key-value codebook to replace activations based on similarity to past mistakes. dafnet extends this paradigm with intra-and interediting attention flows to semantically fuse edits and prevent forgetting. wise @xcite addresses the tradeoff between reliability, generalization, and locality by introducing a dual-memory system: a main memory for pretrained knowledge and a side memory for edits. a routing mechanism selects which memory to use at inference, while sharding stores edits in disjoint subspaces to reduce interference and support scalable updates. another set of methods, lemoe @xcite , elder @xcite , baft @xcite , and memoir @xcite , adopt modular or adaptive architectures to manage sequential edits effectively. lemoe employs a mixture of experts adaptor with tailored module insertion, kv anchor routing, and clustering-based edit ordering. elder uses a router network to direct queries to appropriate lora adapters and introduces a deferral mechanism to retain core model capabilities. baft performs input-dependent, nonlinear fine-tuning in a learned subspace, enhancing the balance between edit success and locality. memoir improves scalability by sparsely updating a residual memory module via sampledependent masks, enabling thousands of edits with strong generalization and minimal interference. rledit @xcite reinterprets hypernetwork-based editing as a reinforcement learning problem, treating editing loss as reward and optimizing hypernetwork parameters across edit sequences. this enables efficient, precise updates that adapt to llm parameter changes over time. prune @xcite proposes a condition number-based constraint that limits perturbations introduced during sequential editing, preserving the model's stability and generalization. finally, recipe @xcite bridges retrieval and continual model editing by encoding edits as compact prompts prepended to the input. a knowledge sentinel dynamically determines whether to retrieve an edit prompt for a given query, improving relevance and inference-time efficiency. ## conclusions adapting large language models to evolving knowledge is essential for maintaining their relevance and effectiveness. this survey explores three key adaptation methods -continual learning for large-scale updates, model editing for precise modifications, and retrieval-based approaches for external knowledge access without altering model parameters. we examine how these methods support updates across factual, domain-specific, language, and user preference knowledge while addressing challenges like scalability, controllability, and efficiency. by consolidating research and presenting a structured taxonomy, this survey provides insights into current strategies and future directions, promoting the development of more adaptable and efficient large language models.