How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023)

Abstract: Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning deployed LLMs with the ever-changing world knowledge. We categorize research works systemically and provide in-depth comparisons and discussions. We also discuss existing challenges and highlight future directions to facilitate research in this field.

## introduction large language models (llms) @xcite @xcite @xcite @xcite trained on massive corpora from various sources (e.g., wikipedia, books, github) implicitly store enormous amounts of world knowledge in their parameters @xcite @xcite , enabling them to act as versatile foundation models for performing various natural language processing (nlp) tasks directly through in-context learning @xcite @xcite or for further fine-tuning for domain-specific uses @xcite @xcite . despite their impressive performance, llms are static after deployment, and there is no mechanism to update themselves or adapt to a changing environment @xcite . our world, however, is dynamic and constantly evolving. as shown in fig. 1 , the static nature of trained llms makes the memorized knowledge figure 1 : a trained llm is static and can be outdated (e.g., chatgpt; openai 2022). how can llms be aligned to the ever-changing world knowledge efficiently and effectively? quickly obsolete, which often causes hallucinations, rendering them unreliable for knowledge-intensive tasks @xcite @xcite . in the era of llms, ensuring their alignment with the ever-changing world knowledge and maintaining their up-to-date status after deployment is a pressing concern because many users and downstream applications rely on them. unfortunately, simply re-training llms with the latest information is infeasible due to prohibitive costs @xcite . intuitively, to update an llm, one can either replace the obsolete knowledge stored implicitly in the model with new ones by modifying its parameters, or override the outdated model outputs using new information explicitly retrieved from the world. tremendous work has been proposed in the literature to implicitly or explicitly refresh deployed llms; however, these approaches, scattered among various tasks, have not been systematically reviewed and analyzed. in this review, we survey the recent compelling advances in aligning deployed llms with the everchanging world knowledge. we categorize research works systemically and highlight representative approaches in each category ( §2) and provide indepth comparison with discussion for insights ( §3). lastly, we discuss potential future directions to @xcite single-stage ic-retrieval @xcite , ic-ralm @xcite , aar @xcite , ike @xcite , adaptive retrieval @xcite , replug @xcite multi-stage ircot @xcite , rarr @xcite , self-ask @xcite , decomp @xcite , react @xcite , art @xcite , llm-augmenter @xcite , dsp @xcite , iter-retgen @xcite , knowledge solver @xcite internetenhanced internet-fewshot @xcite , llm-url @xcite , taskmatrix.ai @xcite , mm-react @xcite , chameleon @xcite , chatgpt plugins (openai, 2023a) facilitate research in this field ( §4). comparison with related work to the best of our knowledge, surveys on this topic are scarce. closest to our work, alkhamissi et al. (2022) review pre-trained language models (lms) as knowledge bases (kbs) and review a set of aspects that a lm should have to fully act as a kb; cao et al. (2023) further divide the life cycle of knowledge in llms into five periods and survey how knowledge circulates; yao et al. (2023b) conduct an empirical analysis of existing knowledge editing methods. despite partially overlapping with our discussion of knowledge editing in §2.1.1, they only touch a subset of the scope that our survey studies and ignore other potentials in aligning llms with the world knowledge. mialon et al. (2023); wang et al. (2023b); qin et al. (2023b) study augmented, interactive, and tool learning of llms respectively, which share different goals from ours. previous knowledge-enhanced lms surveys @xcite @xcite @xcite focus on injecting knowledge into lms, typically requiring modify-ing the model's architecture or re-training. instead, we focus on the potential of how deployed llms capture the ever-changing world knowledge effectively and efficiently without re-training. wang et al. (2023c) provide a comprehensive review of forgetting in deep learning that is not limited to continual learning. pan et al. (2023) review potential approaches that unify knowledge graphs (kgs) and llms. while structural knowledge, such as kgs, can broadly be categorised as explicit knowledge and augmented to llms for new knowledge, kg is static after creation and can still be outdated (ji et al., 2022). new information or discoveries not yet incorporated into kgs may lead to outdated knowledge. however, how to efficiently update kgs is out of the scope of this survey. ## taxonomy of methods based on whether the method tends to directly alter the knowledge stored implicitly in llms, or leverage external resources to override the outdated knowledge, we roughly categorize them as implicit ( §2.1) or explicit ( §2.2) approaches. fig. 2 provides a summary of representative works from each cate-gory (see fig. 6 in appendix for a complete review). detailed descriptions of the methods can be found in appendix a.1. ## comparison and discussion we present the comparison of different methods in discussion of implicit methods ( §2.1). compared to naive re-training or fine-tuning, ke and cl can effectively update obsolete knowledge in llms while minimizing interference on irrelevant ones. we identify their major differences: 1 scale. existing ke methods focus on updating small-scale and localized knowledge, typically on synthetic fact pairs @xcite . while one can perform thousands of edits simultaneously @xcite , updating enormous knowledge in llms may be cumbersome. in contrast, cl enhances models' adaptability via tuning larger-scale parameters, thus updating more knowledge at scale @xcite . however, ke provides fine-grained controllability when specific knowledge needs to be altered, which is unachievable by cl; 2 forgetting. applying ke methods on llms frequently in response to the ever-changing world is sub-optimal due to catastrophic forgetting @xcite ; cl mitigates this issue when learning new knowledge; 3 cost. cl is generally more computationally expensive than ke due to largerscale weight updating. discussion of explicit methods ( §2.2). explicit methods use new knowledge retrieved from the world to override old knowledge in an llm during generation. despite being effective, memoryand retrieval-enhanced methods must periodically maintain the external memory and the knowledge sources in response to the ever-changing world @xcite . conversely, internet-enhanced methods enable real-time knowledge seeking, although potentially suffering from noisy and lowquality web content @xcite . compared to single-stage retrieval, multistage retrieval can solve more complex problems. nevertheless, they may interrupt the generation with multiple retrievals or revisions, leading to considerable inference overheads @xcite . ## challenges and future directions robust and efficient knowledge editing. ke offers fine-grained knowledge updating, which is desirable in some scenarios. despite promising, ke is still in its infancy stage. 1 various knowledge. it is challenging to renew the internal knowledge stored in the parameters of llms, and existing efforts have only explored updating relational knowledge while overlooking other knowledge @xcite ; 2 edit dataset. current ke methods assume edited knowledge pairs exist, which must be annotated beforehand. in reality, how do llms know what knowledge is outdated and thus needs to be updated @xcite ? 3 memorization mechanism. @xcite argue that the localization of specific knowledge via casual tracing may not be reliable, calling for a better understanding of the internal memorization of llms @xcite ; 4 generalization. recent studies @xcite find that existing ke methods show little propagation of edited knowledge, meaning the llm cannot make further reasoning based on the newly acquired knowledge; 5 effectiveness and efficiency. although early efforts have been made @xcite @xcite , methods to effectively, efficiently, and continually renew the knowledge of llms at scale have yet to be thoroughly explored. ## conclusion in this paper, we systematically review recent advances in aligning llms with the ever-changing world knowledge without re-training. we summarize existing approaches and categorize them based on whether they tend to directly alter the knowledge stored implicitly in llms, or leverage external resources to override the outdated knowledge. we comprehensively compare methods of different categories and point out challenges and future directions to facilitate research in this area.