How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Authors: Zihan Zhang
Meng Fang
Ling Chen
Mohammad Reza Namazi Rad
Jun Wang
Venue: EMNLP 2023 Main (2023)
Keywords: large language models
survey
knowledge
TL;DR: A survey on keeping large language models up-to-date.

Abstract: Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment.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.

How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zihan Zhang", Meng Fang?,, Ling Chen', Mohammad-Reza Namazi-Rad', Jun Wang
"University of Technology Sydney *University of Liverpool
University of Wollongong "University College London
Zi Zihan. Zaannn-hnneeeee -reang-5@studente uts. edu. au, Meng. Fang@l ac. uk
Ling. Chen@uts edu. au, mrad@uow. edu. au, junwangêcs ucl ac. uk
Abstract
time
2021.9
2022
Who won the World Cup Qatar?
LLM
France
Although large language models (LLMs) impressive in solving various tasks, they can quickly be outdated after deployment. Main- taining their up-to-date status is pressing con- cern in the current era. This paper provides a comprehensive review recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide de-thpth comparisons and discussion. We also discuss existing challenges and high- light future directions facilitate research in this field
How?
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Who the World Cup Qatar?
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trained LLM is static and can be out- dated (e.g., ChatGPT; OpenAI 2022). How can L.LMs be aligned to the ever-changing world knowledge effi- ciently and effectively?
Figure 1:
quickly obsolete, which often causes hallucinations. rendering them unreliable for knowledge-intent intensive tasks (Lazaridou et al., 2022; Luu et al., 2022: Ji et al., 2023; Si et al., 2023). In the era of LLMs, ensuring their alignment with the ever-changing world knowledge and maintaining their up-to-date status after deployment pressing concern be- cause many users and downstream applications rely on them. Unfortunately, simply re-training L.LMs with the latest information infeasible due to pro- hibitive costs (Patterson et al., 2021).
1 Introduction
Large language models (LLMs) (Brown et al.. 2020; Ouyang et al., 2022; Chowdhery et al., 2022: Zhang et al., 2022: OpenAl, 2023b; Tou- vron et al., 2023; Anil et al., 2023) trained on mas- sive corpora from various sources (e.g., Wikipedia, Books, Github) implicitly store enormous amounts of world knowledge in their parameters (Petroni et al., 2019; Roberts et al., 2020; Jiang et al., 2020), enabling them to act as versatile foundation models for performing various natural language process- ing (NLP) tasks directly through in-context learn- ing (Liu et al., 2023b; OpenAI, 2023b; Bubeck et al., 2023; Kamalloo al., 2023) or for further fine-tuning for domain-specific uses (Singhal et al., 2022; Google, 2023; Liu and Low, 2023).
Intuitively, to update an LLM, one can either replace the obsolete knowledge stored implicitly in the model with new ones by modifying its pa- rameters, 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 de- ployed LLMs; however, these approaches, scat- tered among various tasks, have not been systemat- ically reviewed and analyzed.
Despite their impressive performance, LLMs are static after deployment, and there no mechanism to update themselves or adapt to changing envi- ronment (Kasai et al., 2022; Bubeck et al., 2023). Our world, however, is dynamic and constantly evolving. As shown in 1, the static nature of trained LLMs makes the memorized knowledge
In this review, we survey the recent compelling advances in aligning deployed LLMs with the ever- changing world knowledge. We categorize research works systemically and highlight representative approaches in each category ($2) and provide in- depth comparison with discussion for insights ($3). Lastly, we discuss potential future directions to
"quual contribution
'We release the paper list at https://gittee htatthuu.cooo/ http:///aaessmeeeeeeeeeee 1ilmshinng-lms 11ms and will periodically update it.

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-osming
Biitsble Thiming (Siniisin
RECKONING al., 2033)
Hlpeenewack Feltor
Knowlasgs-lfiter
Cao al, 2011 MEND (Mishell al., SLAGG 20236), REMEDI (llemander el
Diciilation (Padmonation et 20235
Knowlodge Editing
Knowkodge Naurone (Dai all 9027,, ROME (Mong et al, 2022a, MEMIT (Meng at al, 2003),
MEMITeS i (Clagta al., PMET (M03),
20236),
(20231) Cleaa
and
Zisng. 2023)
KRL M (Wio al, 2023), RippleFalis (Cohan et al. 2023), Wang at al. (20250) Nu (Zheng
Implicii (82.1)
Regulatiztion basell
Rez Adamm (Chun al., 2030), DSA (Ke et 3023)
Replay-hased
BLLE (Qin et al. 2022), CTO (Scialom al., 2022)
K-Adspier (Wang a al,, 2021), FL. (Qin en all, 2022), CK1 clang al. 20226). CPT (Ke ei al. 2022), Lifeling Mobl (Chen al., 2022at, Module- Forner (Shen et al. 2023)
tra-trannng
Architectural- based
Coniiuad Leariin
Lidslong Fre training (Jn et th,, 2022), CKL (lang et al., 20226), KILM (Xu et al., 20233), Sebdem (Peng al., 2023b), CaMeLS (Ila et al., 2023), Yu and (2023), Gapto et al. (2023b)
Odtecs
MLM wlige with ever changing world knowlodge
Corcauul Knowkedge Editing
CMR (Lin et al., 2002). Cloplugia (Les et al., 20223) Transfommer Potcher (Heang et al., 2025), GRACE (laatvigenn et all, 2023)
Coepus Documenis
ENN-IM (Khondeleal et al., 2000) AdayfRes (He at al. 2011a),, RetMMatton (Alen et all, 2022), Bhardwy al. (2022), ANN-grcoopt (SBi et al. 2022), SoMon (Peng al., 20250)
Memory- enhancal
Feedback or Cotietions
FaNer (Tandon et al, 2022), MemPrompe IMidias et al., 2022), Teachile (Daiv) Mishra et tl. 2022), SERAC (Mishhlll et al, 20226), MeLLo (Zhong el al. 2023)
[C-Retrieva (Si al., 2023), (C-RALM (Ram al., 2035 AAR (Yu al., 20136), IKE (Zheng as al., 2023), Adaptive Ramieval (Mallen et al., 2023), RePtug (Sa1 et di. 2023b)
Single Stage
Explicit (32.2)
Ratricall onhanced
IRCoT (Trivedi
2023), RARR (Cao al. 2023), Selc-Ask (Press al., 2023), DeccupP (Khot et all, 2023), ReAct (Yau ah., 2023a), ART (Pannsipe at al., 2023), M-ggmeeeee (Peng et all, 20254), DIS (Khuttah et al., 2023), ResRetiin (Shin et tl., 2023), Knooladge
Mult: Stage
Sulver (Feng el al., 2023)
Internet- enhanced
Insemes Fowahar (Lazardoou et al., 2022) ULM-UUL iZieass et al.. 2025), TesisMaaraa, Al (Liang et al., 2025), MM REACT (Yang al., 2025), Chamelonn (La et al. 2023), ClaRPPT Plagins (OgesAl, 2023a)
Figure 2: Taxonomy of methods to align LLMs with the ever-changing world knowledge (due to space limitation, please refer to Appendix A.2 for a complete review). Implicit means the approaches seek to directly alter the knowledge stored in LLMs (e.g., parameters) ($2.1), while Explicit means more often incorporating external resources to override internal knowledge (e.g., search engine) ($2.2).
facilitate research in this field ($4).
ing the model's architecture or re-training, Instead, we focus on the potential of how deployed LLMs capture the ever-changing world knowledge effee- tively and efficiently without re- 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 poten- tial approaches that unify knowledge graphs (KGs) and LLMs. While structural knowledge, such as KGs, can broadly be categorised as explicit knowl- edge 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.
Comparison with Related Work To the best of our knowledge, surveys on this topic are scarce. Closest to our work, AlKhamissi et al. (2022) re- view pre-trained language models (LMs) as knowl- edge 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 empiri- cal analysis of existing knowledge editing meth- ods. Despite partially overlapping with our dis- cussion of knowledge editing in 12.1.1, they only touch a subset of the scope that our survey stud- ies and ignore other potentials in aligning LLMs with the world knowledge. Mialon et al. (2023); Wang et al. (2023b); Qin et al. (2023b) study aug- mented, interactive, and tool learning of LLMs respectively, which share different goals from ours. Previous knowledge-enhanced LMs surveys (Zhu et al., 2021; Wei et al., 2021; Yu et al., 2022; Yin et al., 2022; Zhen et al., 2022) focus on injecting knowledge into LMs, typically requiring modify-
Taxonomy of Methods
Based on whether the method tends to directly al- ter 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-

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gory (See Fig.6 6 in Appendix for complete review), Detailed descriptions of the methods can be found in Appendix A.I.
et al., 2021; Mitchell et al., 2022a). Sinitsin et al. (2020) propose model-agnostic meta-learning- based (Finn et al., 2017) method that trains neural networks in a way that the trained parameters can be easily edited afterwards. Chen et al. (2023c) in- troduce a two-loop framework. In the inner training loop, they employ few gradient updates to enable pre-trained GPT-2 model (Radford et al., 2019) to efficiently memorize external knowledge. Sub- sequently, in the outer loop, the model parameters are dynamically adjusted through optimal meta- parameter learning to incorporate additional knowl- edge that aids reasoning tasks.
2.1 Implicitly Align LLMs with World Knowledge
Previous studies have shown that LLMs can implic- itly memorize knowledge in their large number of parameters after being pre-trained on massive cor- pora (Petroni et al., 2019; Roberts et al., 2020; Jiang et al., 2020; Singhal al., 2022). To keep LLMs up-to-date and align with the current world knowl- edge, the straightforward way is to alter the model's behaviour from themselves to generate desired out- puts. Naively, one can regularly re-train the model from scratch or fine-tune the model with the lat- est corpora to align with current world knowledge. However, re-training is expensive and environmen- tally unfriendly (Patterson et al., 2021), especially in the era of LLMs with billions of parameters. Fine-tuning without constraints may have "but- terfly effect" and affect other knowledge or skills present in the model (Kirkpatrick et al., 2017; Li et al., 2022; AlKhamissi et al., 2022). To cope with this issue, this line of work aims to design better strategies to modify the internal states of LLMs in more controllable and efficient way, which can be categorized into knowledge editing ($2.1.1) and continual learning ($2.1.2).
Hy Hypeenettorr Editor. In contrast to pre- modifying the pre-trained language model, an al- ternative approach in the field involves training extrinsic editors that update knowledge during test time, thereby avoiding any modifications to the base model. De Cao et al. (2021) reframe editing the knowledge of a model as a conniinggtt-uudaae problem. Specifically, given a single data instance that needs to be updated, their trained hypernet- work (Ha et al., 2017) predicts a shift AØ such that O' = 0 + A0, where A is the original pre-traincd LM weights and 6' is the updated weights. To keep editing effective while being easy scale to larger LMs with billions of parameters, Mitchell et al. (2022a) decompose weight updates into low-rank components (Hu et al., 2022), thus making it possi- ble to scale LLMs. Orthogonal to Mitchell et al. (2022a), Hase et al. (2023b) introduce new train- ing objective considering sequential, local, and gen- cralizing model updates. Although scaled beyond single edit, their edit success rate significantly degrades when performing larger edits simultane- ously. Unlike the above methods that operate on the model's weight, Hernandez al. (2023) per- form edits on the representation level. Padmanab- han et al. (2023) employ knowledge distillation to transfer knowledge generated from a teacher model to a student model.
2.1.1 Knowledge Editing
Since tuming LLMs to learn new knowledge can be prohibitively expensive (Patterson et al., 2021), ), re- searchers seek efficient methods to directly update more specific, localized, or fine-grained knowledge that is preserved in LLMs (Mitchell et al., 2022a). Knowledge editing (KE) is arising and promis- ing research area that aims to alter the parameters of some specific knowledge stored in pre-trained models so that the model can make new predictions on those revised instances while keeping other irrel- evant knowledge unchanged (Sinitsin et al., 2020; De Cao et al., 2021; Mitchell et al., 2022a; Meng et al., 2022a; Hase et al., 2023b; Meng et al., 2023). In this section, we categorize existing methods into meta-learning, hypernetwork, and locate-and-edit -based methods.
Locate and Edit. Generally, this line of work adopts the locate and edit pattern: they first iden- tify the location of specific knowledge stored in the model via different assumptions, then directly mod- ify the weights or representations update knowl- edge. Inspired by the findings that feed-forward networks (FFN) in Transformer (Vaswani et al., 2017) are key-value memories (Geva et al., 2021). Dai et al. (2022) introduce the knowledge neurons
This line of work generally fo- cuses on the intrinsic editability of the model itself, aiming to modify the model parameters so that they can be easily updated during inference (De Cao
Meta-learning.

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Who the World Cup Qatar?
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Knowledge Editing
LLM
LLM
LLM
LLM
Who won the World Cup in Qatar?
Continual Learning
LLM
L.M
LLM
LLM
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won World Cup in Qatar?
Retrieval-
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based
Figure 3: high-level comparison different approaches.
concept and propose gradient-based knowledge attribution method to identify these knowledge neu- rons in FFNs. Further, without fine-tuning, they directly modify the corresponding value slots (e.g.. embeddings) in the located knowledge neurons and successfully update or delete knowledge, demon- strating a preliminary potential to edit knowledge in LMs.
cess of factual associations in auto rugo-segressive LMs, opening new research directions for knowledge lo- calization and model editing.
Wu et al. (2023) propose an evaluation framework and dataset for measuring the effective- ness of knowledge editing of L.MMs, well as the ability reason with the altered knowledge and cross-lingual knowledge transfer. Similarly, Cohen al. (2023) evaluate the implications of edit on related facts and show that existing methods fail introduce consistent changes in the model's knowl- edge. Ju and Zhang (2023) propose an evaluation benchmark for locate-and-edit-base methods, aim- ing to reassess the validity of the locality hypothesis of factual knowledge. Wang et al. (2023a) and Xu et al. (2023b) take multilingual into account and extend existing knowledge editing methods into cross-lingual scenarios.
Other.
Different from Geva et al. (2021)'s per-neuron view, Meng et al. (2022a) conduct casual tracing analysis GPT-2 and hypothesize that the Trans- former MLP can be viewed a linear associa- tive memory. They verify their hypothesis by di- rectly updating the middle-layer MLP weights with a rank-one update (Bau et al., 2020). Following Meng et al. (2022a)'s work, Meng et al. (2023) propose a scalable multi-layer method to update an LLM with thousands of facts simultaneously, sig- nificantly improves editing efficiency while main- taining generalization and specificity. Gupla al. (2023a) further adapt it to fix commonsense mis- takes. et al. (2023b) find that Multi-Head Self- Attention (MHSA) weights do not require updating when introducing new knowledge. Based on this, they propose precisely updating FFN weights by simultaneously optimizing the Transformer compo- nent hidden states of MHSA and FFN to memorize target knowledge. Chen al. (2023b) propose an cetteeecttur-daapted multilingual integrated gradi- ents method localize knowledge neurons pre- cisely across multiple architectures and languages. Geva et al. (2023) analyze the internal recall pro-
2.1.2 Continual Learning
Continual learning (CL) aims enable a model to learn from a continuous data stream across time while reducing catastrophic forgetting of previously acquired knowledge (Biesialska et al., 2020). With CL., deployed LLM has the potential to adapt to the changing world without costly re-training from scratch (Bubeck al., 2023). In this section, we introduce approaches that employ CL for aligning LLMss with the current world knowledge, including continual pre-training and continual knowledge editing.

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Continual Pre-training. Unlike traditional con- tinual learning, which sequentially fine-tunes a pre-trained LM on some specific downstream tasks (e.g., QA, text classification), continual pre- training is used to further pre-train an LM to ac- quire new knowledge, where the data corpus is usually unsupervised (Gururangan et al., 2020; Ke and Liu, 2023). Since our target is the versatile foundation LLMs (e.g., GPT-4) that can be applied to many different use cases rather than a fine-tuned model designed for a specific task, we focus on the literature on continual pre-training.
gradual decay mix-ratio to adjust the quantity of the pre-training corpus mixed in the new data when learning sequentially. ELLE (Qin et al., 2022) and CTO (Scialom et al., 2022) also mix the old data while learning new data. However, ELLE starts the pre-training from newly initialized and relatively small BERT (Devlin et al., 2019) and GPT (Radford et al., 2018), while CTO continues learning from TO-3B (Sanh al., 2022), a pre- trained and instruction-tune model.
Architectural. These methods typically al- leviate forgetting by using different subsets of pa- rameters for distinct tasks or domains. Wang et al. (2021); Hu et al. (2022); Ke et al. (2022) freeze the original parameters of the LM to preserve the learned knowledge and add lightweight tunable pa- rameters for continually learning new knowledge. Wang et al. (2021) add separate adapters (Houlsby et al., 2019) for each new task, while Ke et al. (2022) let all domains share adapters and employ task masks to protect critical neurons from being updated. DEMix-DAN -DPT (Gururangan et al., 2022) replaces every FFN layer in Transformer with a separate domain expert mixture layer, containing one expert per domain. When learning new knowl- edge, they only train the newly added expert in each DEMix layer while fixing all other experts. Similarly, Lifelong-Mii MoE (Chen et al., 2023a) pro- gressively expands experts to increase model ca- pacity for learning new knowledge, and mitigates forgetting by freezing previously trained experts and gatings with output- regularization. Qin et al. (2022) enlarge the model's width and depth to attain learning efficiency and employ memory replay to reduce forgetting.
Early works (Gururangan et al., 2020; Röttger and Pierrehumbert, 2021; Lazaridou et al., 2021; Dhingra et al., 2022) empirically analyze continu- ing LM pre-training on emerging domain tem- poral data, showing the potential to update the base LM with new knowledge. Jang et al. (2022b) explicitly categorize world knowledge as time- invariant, outdated, and new knowledge, which should be retained, acquired, and updated respec- tively by an LM when learning continually. Jin et al. (2022); Jang et al. (2022a,b) additionally im- plement traditional CL methods to alleviate catas- trophic forgetting, a phenomenon in which previ- ously learned knowledge or abilities are degraded due to overwritten parameters (Kirkpatrick al., 2017). Among the literature, CL. methods can be mainly categorized into I Regularization, 2 Re- play, and 3 Architectural -based methods.
I Regularization. To mitigate forgetting, regularization-based methods apply regulations to penalize the changes of the critical parameters learned from previous data. Chen et al. (2020) improve the traditional EWC (Kirkpatrick et al., 2017) by recalling previously learned knowledge through the pre-trained parameters, and the method continually learns new information using a multi- task learning objective. Ke et al. (2023) compute the importance of each unit (i.e., attention head and neuron) to the general knowledge in the LM us- ing a proxy based on model robustness to preserve learned knowledge, When continually learning new domains, the approach prevents catastrophic for- getting of the general and domain knowledge and encourages knowledge transfer via soft-masking and contrastive loss.
4 Other Methods. Hu et al. (2023) meta- veta-trains an importance-wiightin model to reweight the per- token loss of the continual data stream, intending to quickly adapt the base LM to new knowledge. Peng et al. (2023b) apply ANN-LM (Khandelwal et al., 2020) to continual learning from streaming data and selectively store hard cases in a non-parametric memory, significantly improving the data-wise and model-wise scalability. Yu and Ji (2023) assess self- inlf--ofooattoouppeee update in LLMs via CL and miti- gate exposure bias by incorporating the selection of relevant facts into training losses.
These methods generally reduce forgetting by replaying previous training data when learning new data. Assuming that the initial pre- training corpus is available, He et al. (2021b) use
Replay.
Continual Knowledge Editing. Lin et al. (2022); Lee et (2022a); Huang et al. (2023) and Hartvigsen et al. (2023) propose a more realistic setting that a deployed LM should be constantly

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No
Black
Category
Representative
Angmentation
Training
-box
methods (Karpukhin et al., 2020; Guu et al.. 2020; Lewis et al., 2020; Izacard al., 2022; Borgeaud et al., 2022; Jiang et al., 2022; Kaur et al., 2022) usually jointly train a retriever and an LM in an end- to-end fashion, making challenging to apply to a deployed LLM (e.g.. GPT-3). Recently, researchers have focused on equipping a fixed LLM with ex- ternal memory (memory- $2.2.1). 1), an off- the-shelf retriever (retrieval-enhancee $2.2.2), Internet (Internel-enhanced: $2.2.3) cope with this issue. Knowledge Editing
MEND (Mitchell et al., 2022a)
TS (I)
#
auxiliary model
x
x
(Meng al., 2022a)
CaiNET (Dong al., 2023)
T5 (0.7B)
%
+params
x
x
MEMIT (Meng al., 2023)
GPT-NeoX (20B)
x
Contioual Learning
K K-Adapter (Wang al., 2021)
RoBERTa (0.3B)
8
*params
x
x
CTO (Scialom el al., 2022)
(3B)
memory
x
DSA (Ke et 2023)
RoBERIa (0.1B)
x
x
Memory-euhanced
MemPrompe (Madaan et al., 2023)
(1758)
memory
SERAC (Mitchell al., 20226)
T5 (0.7B)
*
+auxiliary model
memory
x
MeLLo (Chong al.,. 2025)
GPT-3.5 (1758)
memory memoory-cttrieee
Retrieval-cuhanve
IRCoT (Trivedi al., 2022)
GPT-3.5 (175B)
+8
retriever
RARR (Gan et al., 2023)
PaLM (540B)
*
4ouxiliary model
search engine
DevomP (Khol et al., 2023)
GPT-3 (1758B)
*
retriever
ReAct (Yao al., 2023a)
PaLM (540B)
86
search engine
HLARE (liang ol., 20233
GPT-3.5 (1758)
*
retrizverfecsrch engine
Internet-enhanced
Lazaridou al. (2022)
Gopher (2808)
86
scarch englnne
V
CRITIC (Gou et al., 2023)
G.T (175B)
*
various tools
Chameleon al., 2023)
GPT-4
various tools
Table I: Comparison between representative methods (refer to Appendix A.2 for complete review). means the parameters of the original LM are modified, while * means they are unchanged; Augmentation means additional components used; No Training indicates method does not require additional training; Black-box refers to whether the method suits non-publicly available models (e.g., model architecture, parameters, activations, gradients are available).
corrected to fix its prediction errors, showing the potential to align the model with the latest world knowledge. Lin et al. (2022) benchmark the con- tinual model refinement problem by implementing traditional CL methods. Lee et al. (2022a) and Hartvigsen et al. (2023) freeze the LM's original parameters and continually introduce trainable neu- rons to the FFN layer to rectify problematic model behaviors. In contrast, Hartvigsen al. (2023) learn cache chosen layer's activations in key-value- codebook and retrieve activations when previous similar edits have been performed. Without influencing unrelated inputs, can effi- ciently edit the model thousands of times in a row while generalizing edits to previously unseen in- puts.
2.2.1 Memory-enhanced Methods
Pairing static LLM with growing non- parametric memory enables it to capture informa- tion beyond its memorized knowledge during infer- ence (Wu et al., 2022). The external memory can store recent corpus or feedback that contains new information guide the model generation.
2.2 Explicitly Align LLMs with World Knowledge
Although altering the knowledge implicitly stored in LLMS has shown to be effective (Jang et al., 2022b; Meng et al., 2023), it remains unclear whether it will affect the models' general abilities due to the complexity of neural networks. In con- trast, explicitly augmenting LLMs with the latest information retrieved from various sources can ef- fectively adapt the models to new world knowledge without affecting the original LLMs (Mialon et al., 2023). However, previous retrieval-aus -aggented
Storing Corpus or Documents. ANN-LM (Khandelwal et al., 2020) stores every <context, token> as key-value pairs from corpus in mem- ory. During inference, calculates the probabil- ity of the next token by interpolating fixed LM with a distribution retrieved from the k nearest kens in the memory. Following this vein, He et al. 12021a); Drozdov et al. (2022); Alon et al. (2022) improve the efficiency of ANN-LM by skipping

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Single-Stag' -Stae
Multi-Stage
2020), this line of work designs better retrieval strategies incorporate world knowledge into a fixed LLM through prompting, which can be di- vided into single-stage and multi-stage (Fig.4).
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World Cup Qatar?
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Single-Stage. To ground the model with external knowledge during generation, Ram et al. (2023); Si ct al. (2023) adopt zero-shot and few-shot retrieval respectively and directly prepend the retrieved doc- uments to the input without changing the base LLM. Zheng et al. (2023) retrieve similar edit demon- strations for each input and perform in-context knowledge editing. Compared with gradient- brasient-based knowledge editing ($2.1.1), they have competitive editing performance with fewer side effects. Ar- guing that the general-purpose retrievers could be sub-optimal, Yu et al. (2023b) adopt a small source LM to provide LM-preferred signals to train an adaptive retriever. Mallen et al. (2023) employ heuristic based on entity popularity and only trieve relevant context when the input questions are less popular, which improves performance and re- duces inference costs. Unlike above, to address the limited model's context length, Shi et al. (2023b) prepend cach retrieved document separately to an LLM and then ensemble output probabilities from different passes.
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Figure 4: Single-Stage (left) typically retrieves once, while Multi-Stage (right) involves multiple retrievals revisions to solve complex questions ($2.2.2).
unnecessary retrieval. Meng et al. (2022b) build an additional graph neural network to aggregate information from the retrieved context for better generation. Peng et al. (2023b) improve the scal- ability of KNN-LM for continual learning, while Shi et al. (2022) apply it for zero-shot inference on downstream tasks.
Storing Feedback or Corrections. Inspired by the fact that humans can learn from past mistakes, this line of work stores user feedback in memory to fix the model's 's problematic predictions and avoids similar errors in the future. By querying the mem- ory, the base LLM gains editability to update its outdated knowledge. Kassner al. (2021); Tandon al. (2022) train an auxiliary corrector to apply feedback to repair the model output. Dalvi Mishra et al. (2022) allow users to interact with the system to check its facts and reasoning and correct it when it is wrong. Similarly, Madaan et al. (2022) equip GPT-3 with growing memory, where the key is misunderstanding question, and the value is the corrective feedback. Instead of storing user feed- back, Mitchell et al. (2022b): Zhong al. (2023) explicitly preserve updated knowledge in memory. Given an input, Mitchell et al. (2022b) first apply a classifier to determine a relevant edit exists in the memory and perform knowledge updating through a counterfactual model. Conversely, Zhong et al. (2023) decompose complex questions and ask the base model generate temporary answer. They revise the model output when the generated answer contradicts the retrieved facts from memory.
When solving complex questions, retrieving information only once based on the in- put is often inadequate. This branch of work aims to transform single-stage retrieval into multi-stage retrieval in order to solve complex tasks, usually by leveraging reasoning. Trivedi et al. (2022) inter- leave knowledge retrieval with chain-of -ftouuugtttttts (CoT; Wei et al. 2022) generation to solve complex multi-step reasoning questions. Similarly, Press et al. (2023); Khot et al. (2023); Yao et al. (2023a); Jiang et al. (2023); Shao et al. (2023) decompose questions into sub-questions to provide a specific context for retrieval with model generation. Paran- jape et al. (2023); Chen et al. (2023d); Inaba et al. (2023) further enable the usage of different tools to solve various tasks. Unlike the simple retrieve- then-read paradigm, Khattab et al. (2023) pass in- termediate messages between an LLM and a re- triever; Gao et al. (2023); He et al. (2022); Zhao et al. (2023); Yu et al. (2023a) retrieve after gen- eration and perform post-edit revisions for more faithful outputs. Peng al. (2023a) iteratively revise ChatGPT to improve model responses us- ing feedback and external knowledge. Feng et al.
Multi-Stage.
2.2.2 Retrieval-enhanced Methods
Leveraging an tff-the-shelf retriever and the in- context learning ability of LLMs (Brown et al.,

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(2023) teach LLMs themselves to search for knowl- edge from external knowledge graphs (KGs) via prompting and simplify searching into a multi-hop decision sequence, allowing explainable decision- making of the processes.
when specific knowledge needs to be altered, which is unachievable by CL: ② Forgetting. Applying KE methods on LLMs frequently response to the ever-changing world is sub-optimal due to catas- trophic forgetting (Huang et al., 2023; Hartvigsen et al., 2023); CL mitigates this issue when learning new knowledge: + Cost. CL is generally more computationally expensive than KE due to larger- scale weight updating.
2.2.3 Internet-enhanced Methods
Prior retrieval- -uggented work relies on static or offline knowledge sources (e.g., Wikipedia dump), which may not be sufficiently up-to-date or com- plete for tasks that require the latest knowledge (Kasai et al., 2022; Zhang et al., 2023; Li et al., 2023a). A recent trend uses the whole web as the knowledge source and equips LLMs with the Internet to support real-time information seeking (Nakano al., 2022; Menick al., 2022; Komeili et al., 2022; Shuster et al., 2022; Qin et al., 2023a; Liu et al., 2023a). Lazaridou et al. (2022) augment few-shot QA prompting with the context retrieved from Google search. Press et al. (2023); Jiang et al. (2023) interleave reasoning with web search. Re- cently, tools such as LangChain (Chase, 2022) and ChatGPT Plugins (OpenAI, 2023a) connect a de- ployed LLM to the Internet without training, mak- ing them more powerful for solving knowledge- intensive tasks. Beyond search engines, Yao et al. (2023a); Liang et al. (2023); Paranjape et al. (2023); Yang et al. (2023); Gou et al. (2023); Lu et al. (2023) treat LLMs as central planners and compose various plug-and-play tools for solving complex questions.
Discussion of Explicit Methods ($2.2). Explicit methods use new knowledge retrieved from the world to override old knowledge in an LLM dur- ing generation. Despite being effective, memory- and retrieval-enhanced methods must periodically maintain the external memory and the knowledge sources in response to the ever-changing world (Ka- sai et al., 2022). Conversely, Internet-enhanced methods enable real-time knowledge seeking, al- though potentially suffering from noisy and low- quality web content (Li et al., 2023a; Luo et al., 2023). Compared to single-stage retrieval, multi- stage retrieval can solve more complex problems. Nevertheless, they may interrupt the generation with multiple retrievals or revisions, leading to con- siderable inference overheads (Shao et al., 2023).
Updating LLMs Implicitly or Explicitly? We observe increasing trend of explicitly aligning LLMs with world knowledge while keeping the model untouched (Table 3 Appendix). Com- pared to explicit approaches: ! Applicability. Im- plicit methods usually require modifying LLM's parameters or gradients, making it challenging to update closed-source models; 2 Side Effects. Al- though constraints have been added to avoid edit- ing irrelevant knowledge (Mitchell al., 2022a; Meng et al., 2023) or forgetting general knowl- edge (Jang et al., 2022b), modifying the LLM's parameters inevitably has side effects that may hurt the performance, which is hard to estimate due to the complexity of neural networks (Brown et al., 2023); 3 Efficiency. Implicit methods typically require training, while most explicit methods lever- age fixed LLM and an off-the-shell retriever, cras- ing the necessity of training. However, explicit methods do not directly modify the intrinsic knowl- edge within LLMs,, but instead rely on on-the-fly retrieval during inference, resulting in a notable increase the computational cost of inference.
3 Comparison and Discussion
We present the comparison of different methods in Table 1 and in Fig.3, and the characteristics of different methods Table in Appendix.
Discussion of Implicit Methods ($2.1). Com- pared to naive re- 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: ! Scale. Existing KE methods focus on updating small-scale and localized knowledge, typically on synthetic fact pairs (Mitchell et al., 2022a; Meng et al., 2022a). While one can perform thousands of edits simultancously (Meng et al., 2023), updating enormous knowledge in LLMs may be cumber- some. In contrast, CL enhances models' adaptabil- ity via tuning larger-seale parameters, thus updat- ing more knowledge at scale (Jang et al., 2022b). However, KE provides fine-gre -rained controllability

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4 Challenges and Future Directions
and LLMs sometimes favour their internal knowl- edge rather than the provided context during gen- eration (an example in Fig.5; Neeman et al. 2022; Li et al. 2022; Chen et al. 2022). While initial at- tempts have been made (Mallen et al., 2023; Zhou et al., 2023; Xie et al., 2023), they are still limited.
Robust and Efficient Knowledge Editing. KE offers fine- fine-grained knowledge updating, which is desirable in some scenarios. Despite promising, KE is still in its infaney stage. ! Various knowl- edge. It is challenging renew the internal knowl- edge stored in the parameters of LLMs, and ex- isting efforts have only explored updating rela- tional knowledge while overlooking other knowl- edge (Meng et al.. 2023); 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 be updated (Zhang and Choi, 2023; Yin al., 2023)? ④ Memorization mech- anism. Hase et al. (2023a) argue that the localiza- tion of specific knowledge via casual tracing may not be reliable, calling for a better understanding of the internal memorization of LLMs (Tirumala et al., 2022; Carlini et al., 2023); ④ Generaliza- tion. Recent studies (Onoe et al., 2023; Zhong al., 2023) find that existing KE methods show little propagation of edited knowledge, meaning the LLM cannot make further reasoning based on the newly acquired knowledge; c Effectiveness and efficiency. Although early efforts have been made (Hernandez et al., 2023; Huang et al., 2023; Hartvigsen et al., 2023), methods to effectively, ef- ficiently, and continually renew the knowledge of LLMs at scale have yet to be thoroughly explored.
Robust and Efficient Retrieval. Interacting with external resources can cause interruptions during generation, significantly increasing inference over- heads, especially for multi-stage methods that in- volve multiple retrievals or revisions. Potential remedies may be efficient memory management (Peng et al., 2023b; Kang et al., 2023; Cheng et al., 2023) or selective retrieval that only consults exter- nal resources when necessary (Mallen et al., 2023), On the other hand, the retrieved context can be ir- relevant and noisy, which may distract LLMs (Shi et al., 2023a; Luo et al., 2023), or too long, which exceeds the input limits and renders high cost (Shi et al., 2023b).
Comprehensive Evaluation and Benchmarks. Although approaches of different categories can align the trained LLMs with the changing world without re-training, their effectiveness is primarily evaluated on synthetic datasets in specific settings, which might not be comprehensive (Jang et al., 2022a,b; Hoelscher-Obermaice et al., 2023). More- over, although efforts have been made to evaluate KE (Wu al., 2023; Cohen et al., 2023; Ju and Zhang, 2023), there is no quantitative comparison of methods of different categories (i.e., comparing KE vs. CL vs. retrieval -trree methods), hinder- ing their application in different scenarios. Lastly, existing benchmarks are too static to measure the dynamic world, which calls for real-time evalua- tion benchmarks (Liška et al., 2022; Kasai et al., 2022).
Efficient Continual Learning of LLMs. A con- tinual pre-trained LLM can update its internal knowledge and adapt to the changing world, but maintaining the general knowledge required for downstream tasks without forgetting is challeng- ing (Ke and Liu, 2023). Moreover, existing meth- ods are limited to small-scale LMs, leaving CL of LLMs rarely studied. While parameter-efficient tuning (Ding et al., 2022) may be beneficial, it re- mains under-explored to align an LLM with the dynamic world via CL.
5 Conclusion
In this paper, we systematically review recent ad- vances in aligning LLMs with the ever-changing world knowledge without re- re-training We sum- marize existing approaches and categorize them based on whether they tend to directly alter the knowledge stored implicitly in LLMs, leverage external resources to override the outdated knowl- edge. We comprehensively compare methods of different categorics and point out challenges and future directions to facilitate research in this area.
Solving Knowledge Conflicts. Replacing old knowledge with new ones can cause knowledge conflicts regardless of using implicit or explicit methods. For implicit methods, these side effects are only evaluated in specific settings, and there is no idea of how the general skills of LLMs are impacted (Brown et al., 2023). For retrieval-based methods, knowledge retrieved from the world can contradict the knowledge memorized inside LLMss

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Limitations
titative evaluation (benchmarks) is challenging and interesting future direction to fairly compare methods of different categories to align LLMs with updated world knowledge ($4). We will leave quan- titative comparisons and analysis as future work.
In this paper, we systematically review recent ad- vances in aligning LLMs with the ever-changing world knowledge without re-training. We compare our work with the related surveys $1 and will pe- riodically add related approaches. Despite our best efforts, there exist some limitations in this paper:
A Acknowledgements
This work is supported by TPG Telecom. We would like thank anonymous reviewers for their valuable comments.
Scope. In this survey, we do not review knowledge-enhanced approaches that require re- training because we focus on the already trained (deployed) models and how to keep them up-to- date. We refer interested readers to the relevant knowledge-enhanced LMs surveys (Zhu et al., 2021; Wei et al., 2021; Yu et al., 2022; Yin et al., 2022; Zhen et al., 2022). Second, in terms of world knowledge, we focus on text-based knowledge and leave other kinds of knowledge, such as images, video, audio, etc., and structural knowledge, such knowledge graphs (KGs) and databases, for fu- ture work. Third, we mainly review the cutting- edge approaches within three years (mostly in 2022 and 2023) in $2, mainly from the ACL, EMNLP. NAACL, TACL, NeurIPS, ICML,, ICLR, arXiv. De- spite our best efforts, by no means the surveyed methods are complete, and we may miss some im- portant references. Lastly, we cannot afford all the technical details due to page limitations and may only provide brief introductions. We provide additional discussion of approaches in Appendix A.1.
References
Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona Diab, and Marjan Ghazvininejad. 2022. A review language models as knowledge bases.
Uri Alon, Frank Xu, Junxian He, Sudipta Sengupta, Dan Roth, and Graham Neubig. 2022. Neuro-symbolic language modeling with automaton-augmenttt re- trieval. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 468-485 PMLR.
Rohan Anil, Andrew M. Dai, Orhan Firat, Melvin John- son, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey. Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent EI Shafcy, Yanping Huang, Kathy Meier-Hellstern Gau- rav Mishra, Erica Moreira. Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao. Yuanzhong Xu, Yujing Zhang. Gustavo Hermandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry. Christopher A. PboqueetteChoo.. Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du Ethan Dyer. Vlad Feinberg, Fangxiaoyu Feng. Vlad Fienber, Markus Freitag, Xavier Gar- cia, Sebastian Gehrmann, Lucas Gonzalez, Guy Gur- Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, Andrea Hu, Jeffrey Hui, Jeremy Hur- witz, Michael Isard, Abe Ittycheriah, Matthew Jagiel- ski, Wenhao Jia, Kathleen Kencaly, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Ben- jamin Lee, Eric Li, Music Li, Wei Li. YaGuang Li. Jian Li, Hycontack Lim, Hanzhao Lin, Zhongtao Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nys- trom, Alicia Parrish, Marie Pellat, Martin Polacek. Alex Polozov, Reiner Pope. Siyuan Qiao. Emily Reif, Bryan Richter, Parker Riley, Alex Castro Ros, Au- rko Roy, Brennan Saeta, Rajkumar Samuel, Renee Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniel Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang. John Wiet- ing, Yuhuai Wu, Kelvin Xu, Yunhan Xu, Linting
Taxonomy. It should be noted that some ap- proaches are hybrid and can be categorized into different branches. We mainly categorize them based on their main components or mechanism. For instance, all methods in $2.2 require retrieving from external resources. Memory- Memory-enhanced meth- ods (52.2.1) pay more attention to the design of external memory, while paying little attention to retrieval strategies.
Empirical Comparison. We provide detailed comparisons and discussions in $3 and potential future directions in $4. All the conclusions are proposed based on empirical summarization of ex- isting works. However, as the field evolves fast, these empirical conclusions might be inapplicable. We will update the latest opinions timely. In addi- tion, we do not provide quantitative comparisons through experiments since there is no unified eval- uation benchmarks of different categories. Quan-

---

Xue, Pengeheng Yin, Jiahui Yu, Qiao Zhang, Steven Zheng, Ce Zheng. Weikang Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. 2023. Palm 2 technical report.
Boxi Cao, Hongyu Lin, Xianpei Han, and Le Sun. 2023, The life cycle of knowledge in big language models: A survey.
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang 2023. Quantifying memorization across neural lan- guage models. In The Eleventh International Confer- ence on Learning Representations.
Alexei Bacvski and Michael Auli 2019. Adaptive input representations for neural language modeling. In In- lernational Conference on Learning Representations.
David Bau, Steven Liu, Tongzhou Wang, Jun- Yan Zhu,
and Antonio Torralba. 2020. Rewriting a deep gen- erative model. In Computer Vision ECCV 2020, pages 351-369, Cham. Springer International Pub- lishing.
Harrison Chase. 2022. Langchain.
Hung-Ting Chen, Michael Zhang. and Eunsol Choi. 2022. Rich knowledge sources bring complex knowl- edge conflicts: Recalibrating models to reflect con- flicting evidence. In Proceedings of the 2022 Con- ference on Empirical Methods in Natural Language Processing, pages 2292-2307. Abu Dhabi, United Arab Emirates. Association for Computational Lin- guistics.
Rishabh Bhardwaj. George Polovets. and Monica Sunkara. 2022. Adaptation approaches for nearest neighbor language models.
Magdalena Biesialska, Katarzyna Biesialska, and Marta R. Costa-jussà. 2020. Continual lifelong learn- ing in natural language processing: survey. In Proceedings of the 28th International Conference on Computational Linguisties. pages 6523-6541, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, and Xiangzhan Yu, 2020. Recall and learn: Fine-tuning deep pretrained language models with less forgetting. In Proceedings of the 2020 Confer- ence on Empirical Methods in Natural Language Processing (EMNLP), pages 7870-7881, Online. As- sociation for Computational Linguistics.
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai,, Eliza Rutherford, Katie Millican, George van den Driessche, Baptttteette Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas. Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang. Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Si- monyan, Jack W. Rac, Erich Elsen, and Laurent Sifre. 2022. Improving language models by retrieving from trillions of tokens.
Wuyang Chen, Yanqi Zhou, Nan Du Yamping Huang, James Laudon, Zhifeng Chen, and Claire Cui. 2023a. Lifelong language pretraining with distribution- specialized experts. Proceedings of the 40th Inter- national Conference on Machine Learning. volume 202 of Proceedings of Machine Learning Research, pages 5383-5333 5395, PMLR.
Yuheng Chen, Pengfei Cao. Yubo Chen, Kang Liu, and Jun Zhao. 2023b. Journey to the center of the knowl- edge neurons: Discoveries of knowledge neurons and degenerate knowledge neu- rons.
Davis Brown, Charles Godfrey, Cody Nizinski, Jonathan Tu, and Henry Kvinge. 2023. Edit at your own risk: evaluating the robustness of edited models to distribution shifts.
Zeming Chen, Gail Weiss, Eric Mitchell, Asli Celiky- ilmaz, and Antoine Bosselut. 2023c. Reckoning: Reasoning through dynamic knowledge encoding.
Tom Brown. Benjamin Mann, Nick Ryder, Melanie Subbiah. Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger, Tom Henighan. Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz. Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, 1lya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems, volume 33, pages 1877-1901. Curran Associates, Inc.
Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong. Wayne Xin Zhao, and Ji-Rong Wen. 2023d. Chatcot: Tool-augmenned chain of thought reasoning on chat- based large language models.
Xin Cheng, Yankai Lin, Xiuying Chen, Dongyan Zhao, and Rui Yan. 2023. Decouple knowledge from paramters for plug-and- and-play language modeling,
A Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Tay, Noam Shazeer, Vin- odkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat,
Sébastien Bubeck. Varun Chandrasckaran, Ronen EI- dan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Pe- ter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi. Marco Tulio Ribeiro, and Yi Zhang. 2023. Sparks of artificial general in- telligence: Early experiments with gpt-4.

---

Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, An- drew M. Dai, Thanumalayan Sankaranarayana Pil- lai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov. Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei. Kathy Heiiseeellttern Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022. Palm: Scaling language mod- eling with pathways.
parameter efficient methods for pre-trained language models.
Qingxiu Dong, Damai Dai, Yifan Song. Jingjing Xu, Zhifang Sui, and Lei Li. 2022. Calibrating factual knowledge in pretrained language models. In Find- ings of the Association for Computational Linguistics: EMNLP 2022, pages 5937-5947, Abu Dhabi, United Arab Emirates. Association for Computational Lin- guistics.
Andrew Drozdov, Shufan Wang. Razieh Rahimi, An- drew McCallum, Hamed Zamani, and Mohit Tyyer. 2022. You can't 't pick your neighbors, or can you? when and how to rely on retrieval in the kNN-LM. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2997-3007, Abu Dhabi, United Arab Emirates. Association for Com- putational Linguistics.
Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2023. Evaluating the ripple effects of knowledge editing in language models.
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022. Knowledge neurons in pretrained transformers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics /Volume 1: Long Papers), pages 8493- 8502, Dublin, Ireland. Association for Computational Linguistics.
Chao Feng, Xinyu Zhang, and Zichu Fei. 2023. Knowl- edge solver: Teaching lims to search for domain knowledge from knowledge graphs.
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th Interna- tional Conference on Machine Learning Volume 70, ICML' 17, page I 1126-1 135. JMLR.org.
Bhavana Dalvi Mishra, Oyvind Tafjord, and Peter Clark. 2022. Towards teachable reasoning systems: Using a dynamic memory of user feedback for continual sys- tem improvement. In Proceedings of the 2022 Con- serence on Empirical Methods in Natural Language Processing, pages 9465-9480, Abu Dhabi, United Arab Emirates. Association for Computational Lin- guistics.
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan. Vin- cent Y. Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, and Kelvin Guu. 2023. Rarr: Researching and re- vising what language models say, using language models.
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021. Edit- ing factual knowledge in language models. In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 6491 6506, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023. Dissecting recall of factual associ- ations in auto- regressvee language models.
Mor Geva. Roei Schuster, Jonathan Berant, and Omer Levy. 2021. Transformer -orward layers are key- value memories. In Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pages 5484-5495, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Jacob Devlin, Ming-War Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minncapolis, Minnesota. Association for Computational Linguistics.
Google. 2023. Med-palm
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang. Nan Duan, and Weizhu Chen. 2023. Critic: Large language models can self-correct with tool-interactive critiquing.
Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and William W. Cohen. 2022. Time-aware language mod- els as temporal knowledge bases. Transactions of the Association for Computational Linguistics, 10:257 273.
Anshita Gupta, Debanjan Mondal, Akshay Krishna She- shadri, Wenlong Zhao, Xiang Lorraine Li, Sarah Wiegreffe, and Niket Tandon. 2023a. Editing com- monsense knowledge in gpt.
Ning Ding, Yujia Qin. Guang Yang, Fuchao Wei, Zong- han Yang. Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Hai- Zheng, Jianfei Chen, Yang Liu, Jie Tang. Juanzi Li, and Maosong Sun. 2022. Delta tuning: A comprehensive study of
Kshitij Gupta, Benjamin Thérien, Adam Ibrahim. Mats L. Richter, Quentin Anthony, Fugene Belilovsky, Irina Rish, and Timothée Lesort. 2023b. Continual pre-training of large language models: How to (re)warm your model?

---

Suchin Gururangan, Mike Lewis. Ari Holtzman, Noah A. Smith, and Luke Zettlemoyer. 2022. DEMix layers: Disentangling domains for modular language modeling. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistie:: Human Language Tech- nologies, pages 5557-5576, Seattle, United States.
Chapter of the Association for Computational Lin- guisties: Main Volume, pages 1121-1133, Online. Association for Computational Linguisties.
Evan Hernandez, Belinda Z. Li, and Jacob Andreas. 2023. Inspecting and editing knowledge representa- tions in language models.
Association for Computational Linguistics.
Jason Hoelscher-Ohermaiee Julia Persson, Eshen Kran,
Joannis Konstas, and Fazl Barez. 2023. Detecting edit failures in large language models; An improved specificity benchmark.
Suchin Gururangan, Ana Marasović, Swabha Swayandipta, Kyle Lo. Iz Beltagy, Doug Downey, and Noah A. Smith. 2020. Don't I stop pretraining: Adapt language models domains and tasks. In Proceedings of the 58th Annual Meeting of the Association for Compututional Linguistics, pages 8342-8360, Online. Association for Computational Linguistics.
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019, Parameter- transfer learning for nlp. In In- ternational Conference on Machine Learning, pages 2790-2799, PMLR.
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasu- pat, and Mingwei Chang. 2020. Retrieval augmented language model pre-training. In International confer- ence on machine learning, pages 2238. PMLR.
Edward J Hu. yelong shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang. and Weizhu Chen. 2022. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Reprexentations.
David Ha, Andrew M. Dai, and Quoc V. Le. 2017. Hy- pernetworks. In International Conference Learn- ing Representations.
Nathan Hu, Eric Mitchell, Christopher D. Manning, and Chelsea Finn. 2023. Meta-learning online adaptation of language models.
Thomas Hartvigsen, Swami Sankaranarayaman, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2023. Aging with grace: Lifelong model editing with dis- crete key-value adaptors.
Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023. Transformer- patcher: One mistake worth one neuron. In The Eleventh International Conference on Learning Rep- resentations.
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghan- deharioun. 2023a. Does localization inform editing? surprising differences in causality-basee localization vs. knowledge editing in language models. arXiv preprint 2331 233...413...
Tatsuro Inaba, Hirokazu Kiyomaru, Fei Cheng, and Sadao Kurohashi. 2023. Multitool-cot: Gpt-3 can use multiple external tools with chain of thought prompt- ing.
Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zor- nitsa Kozareva, Veselin Stoyanov, Mohit Bansal, and Srinivasan Iyer. 2023b. Methods for measuring, up- dating, and visualizing factual beliefs in language models. In Proceedings of the 17th Conference of the European Chapter of the Association for Compu- tational Linguistics, pages 2714-2731, Dubrovnik, Croatia. Association for Computational Linguistics.
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni. Timo Schick, Jane Dwivedi- Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022. Atlas: Few-shot learning with retrieval augmented language models.
Joel Jang, Sconghyeon Ye, Changho Lec, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, and Minjaon Seo. 2022a. TemporalWiki: A lifelong benchmark for training and evaluating ever-evolving language models. In Proceedings of the 2022 Con- ference on Empirical Methods in Natural Language Processing, pages 6237-6250, Abu Dhabi, United Arab Emirates. Association for Computational Lin- guistics.
Hangfeng He, Hongming Zhang, and Dan Roth. 2022. Rethinking with retrieval: Faithful large language model inference.
Junxian He, Graham Neubig, and Taylor Berg- Kirkpatrick. 2021a. Efficient nearest neighbor lan- guage models. In Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Pro- cessing, pages 5703-5714, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Joel Jang, Sconghycon Ye, Sohee Yang. Joongbo Shin, Janghoon Han, Gyeonghun KIM, Stanley Jungkyu Choi, and Minjoon Seo. 2022b. Towards continual knowledge learning of language models. In Interna- tional Conference on Learning Representations.
Tianxing He, Jun Liu. Kyunghyun Cho. Myle Ott, Bing Liu, James Glass, and Fuchun Peng. 20. Ana- lyzing the forgetting problem in pretrain-finetuning of open-domaie dialogue response models. In Pro- ceedings of the 16th Conference of the European
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Martti- nen, and Philip S. Yu. 2022. A survey on knowledge graphs: Representation, acquisition, and applications.

---

IEEE Transactions on Neural Networks and Learning Systems, 33(2):494-514
Nora Kassner, Oyvind Tafjord, Hinrich Schüitze, and Peter Clark. 2021. BeliefBank: Adding memory to a pre-trained language model for systematic notion of belief. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Process- ing, pages 8849-8861, Online and Punta Cana, Do- minican Republic. Association for Computational Linguistics.
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of halluci- nation in natural language generation. ACM Comput. Surv. 55(1))
Zhengbao Jiang, Luyu Gao, Zhiruo Wang, Jun Araki, Haibo Ding. Jamie Callan, and Graham Neubig. 2022. Retrieval as attention: End-to-end learning of retrieval and reading within single transformer. In Proceedings of the 2022 Conference on Empiri- cal Methods in Natural Language Processing, pages 2336-2349, Abu Dhabi, United Arab Emirates. As- sociation for Computational Linguistics.
Jivat Kaur, Sumit Bhatia, Milan Aggarwal, Rachit Bansal, and Balaji Krishnamurthy, 2022. LM-CORE: Language models with contextually relevant external knowledge. In Findings of the Association for Com- putational Linguistics: NAACL 2022, pages 750-769, Seattle, United States. Association for Computational Linguistics.
Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu, Lei Shu, and Bing Liu. 2022. Continual training of language models for few-shot learning- the 2022 Conference on Empirical Methods in Natu- ral Language Processing, pages 10205-10216, Abu Dhabi, United Arab Emirates. Association for Com- putational Linguistics.
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020. How can we know what language models know? Transactions of the Association for Computational Linguisties, 8:423-438.
Proceedings of
Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu. Jane Dwivedi- Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. Active retrieval augmented generation.
Zixuan Ke and Bing Liu, 2023, Continual learning of natural language processing tasks: A survey.
Xisen Jin, Dejiao Zhang, Henghui Zhu, Wei Xiao, Shang-Wen Li, Xiaokai Wei, Andrew Arnold, and Xiang Ren. 2022. Lifelong pretraining: Continually adapting language models to emerging corpora. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies. pages 4764-4780, Seattle, United States. Association for Computational I Linguistics.
Zixuan Ke, Yijia Shao, Haowei Lin, Tatsuya Konishi, Gyuhak Kim, and Bing Liu. 2023. Continual pre- training of language models. In The Eleventh Inter- national Conference on Learning Representations.
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer. and Mike Lewis. 2020. Generalization through memorization: Nearest neighbor language models. In International Conference on Learning Representations.
Yiming Ju and Zheng Zhang. 2023. Klob: a bench- mark for assessing knowledge locating methods in language models.
Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. 2023. Demonstrate-searc sernssrrate-eearc---reiettt Composing retrieval and language models for knowledge-intesiive nlp.
Ehsan Kamalloo, Nouha Dziri, Charles L. A. Clarke, and Davood Rafiei. 2023. Evaluating open-domain question answering in the era of large language mod- els.
Tushar Khot, Harsh Trivedi. Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sab- harwal. 2023. Decomposed prompting: A modular approach for solving complex tasks. In The Eleventh International Conference on Learning Representa- tions.
Jikun Kang, Romain Laroche, Xindi Yuan, Adam Trischler, Xue Liu, and Jie Fu. 2023. Think before you act: Decision transformers with internal working memory.
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open- domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769-6781, Online. Association for Computational Linguistics.
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz. Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Ag- nieszka Grabska- Barwinska, Demis Hassabis, Clau- dia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017. Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13):321-3352.
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. 2022. Realtime qa: What's the answer right now?
Mojtaba Komeili, Kurt Shuster, and Jason Weston. 2022. Internet-augmenned dialogue generation. Proceed- ings of the 60th Annual Meeting of the Association

---

for Computational Linguistics ( (Volume Long Pa- pers), pages 1860-8478,118 Dublin, Ireland. Association for Computational Linguistics.
Nelson F. Liu, Tianyi Zhang, and Percy Liang. 2023a. Evaluating verifiability in generative search engines.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023b. Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55(9).
Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. 2022. Internet- augmented language models through few-shot prompting for open-domain question answering.
Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gri- bovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi. Mai Gimenez, Cyprien de Masson d' Autume. Tomáš Kočiský, Sebastian Ruder, Dani Yogatama, Kris Cao, Susamnah Young, and Phil Blunsom. 2021. Mind the gap: Assessing temporal generalization in neural language models. In Advances in Neural Information Processing Systems.
Tiedong Liu and Bryan Kian Hsiang Low. 2023, Goat: Fine-tuned Ilama outperforms gpt-4 on arithmetic tasks.
Adam Liska, Tomáš Kočiský, Elena Gribovskaya, Tay- fun Terzi, Eren Sezener. Devang Agrawal, Cyprien de Masson d' Autume. Tim Scholtes, Manzil Zaheer, Susannah Young, Ellln Milisenan-MMMahhh Austin, Phil Blunsom, and Angeliki Lazaridou. 2022. Streamingga: A benchmark for adaptation knowledge over time in question answering models.
Sophia
Kyungjae Lee, Wookje Han, Seung- wonng-wonnoo Hwang, Hwaran Lee, Joonsuk Park, and Sang-Won Moo Lee. 2022a. Plug-and-play adaptation for continuously- updated QA. In Findings of the Association for Com- putational Linguistics: ACL 2022, pages 438-447, Dublin, Ireland. Association for Computational Lin- guistics.
Pan Lu, Baolin Peng, Hao Cheng. Michel Galley, Kai- Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023, Chameleon: Plug-and-play com- positional reasoning with large language models.
Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pas- cale N Fung, Mohammad Shoeybi, and Bryan Catan- zaro. 2022b. Factuality enhanced language mod- els for open-ended text generation. In Advances in Neural Information Processing Systems, volume 35, pages 15586-34599 Curran Associates, Inc.
Hongyin Luo, Yung-Sung Chuang, Yuan Gong, Tian- hua Zhang, Yoon Kim, Xixin Wu, Danny Fox, He- len Meng, and James Glass. 2023. Sail: Search- augmented instruction learning.
Kelvin Luu, Daniel Khashabi, Suchin Gururangan, Kar- ishma Mandyam, and Noah A. Smith. 2022. Time waits for no one! analysis and challenges of tem- poral misalignment. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pages 5944-5958, Seattle, United States. Association for Computational Lin- guistics.
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Hein- rich Küttler, Mike Lewis, Wen-tau Yih, Tim Rock- täschel, et al. 2020. Retrieval-augmented generation for knowledge-intensee nip tasks. Advances in Neu- ral Information Processing Systems, 33:9459-999 9474.
Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang. Michal Lukasik, Andreas Veit, Felix Yu, and Sanjiv Kumar. 2022. Large language models with controllable working memory.
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023. Query rewriting for retrieval- augmented large language models.
Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jingyuan Wang. Jian- Yun Nie, and Ji-Rong Wen. 2023a. The web can be your oyster for improving large language models.
Aman Madaan, Niket Tandon, Peter Clark, and Yim- ing Yang. 2022. Memory- prompt editing to improve GPT-3 after deployment. In Proceed- ings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2833-2861, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Xiaopeng Li, Shasha Li, Shezheng Song, Jing Yang, Jun Ma, and Jie Yu. 2023b. Pmee: Precise model editing in a transformer.
Yaoho Liang, Chenfei Wu, Ting Song, Wenshan Wu, Yan Xia, Yu Liu, Yang Ou, Shuai Lu, Lei Ji, Shaoguang Mao, Yun Wang, Linjun Shou, Ming Gong, and Nan Duan. 2023. Taskmatrix. ai: Com- pleting tasks by connecting foundation models with millions of apis.
Alex Mallen, Akari Asaí, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannanch Hajishirzi. 2023, When not trust language models: Investigating effectiveness of parametric and non-parametric mem- ories.
Bill Yuchen Lin, Sida Wang, Xi Lin, Robin Jia, Lin Xiao, Xiang Ren, and Scott Yih. 2022. On continual model refinement in distriiitttttiinnnn data streams. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics ( (Volume Long Pupers), pages 3128-3139 Dublin, Ireland. Association for Computational Linguistics.
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022a. Locating and editing factual asso- ciations in gpt. In Advances in Neural Information Processing Systems, volume 35, pages 17372. Curran Associates, Inc

---

Kevin Meng, Arnab Sen Sharma, Alex J Andonian. Yonatan Belinkov, and David Bau. 2023. Mass- editing memory in a transformer. In The Fleventh International Conference on Learning Representa- lions.
Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, volume 35, pages 27730-27744. Curran Associates, Inc.
Yuxian Meng, Shi Zong. Xiaoya Li, Xiaofei Sun, Tian- wei Zhang, Fei Wu, and Jiwei Li. 2022b. GNN- LM: Language modeling based on global contexts via GNN. In International Conference on Learning Representations.
Shankar Padmanabhan, Yasumasa Onoe, Michael J. Q. Zhang, Greg Durrett, and Eunsol Choi. 2023. Propa- gating knowledge updates to Ims through distillation.
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Ji- apu Wang, and Xindong Wu. 2023. Unifying large language models and knowledge graphs: A roadmap.
Jacob Menick, Maja Trebacz. Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese Susannah Young, Lucy Camphell- Gillingham. Geoffrey Irving, and Nat McAleese. 2022. Teaching language models to support answers with verified quotes.
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannanch Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro. 2023, Art: Automatic multi- step reasoning and tool-use for large language mod- els.
Grégoire Mialon, Roberto Dessi, Maria Lomeli, Christo- foros Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi- Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, and Thomas Scialom. 2023. Augmented language mod- els: a survey.
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So. Maud Texier, and Jeff Dean. 2021. Carbon emissions and large neural network training.
Baolin Peng. Michel Galley, Pengcheng He, Hao Cheng. Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, and Jianfeng Gao. 2023a. Check your facts and try again: Improving large language models with external knowledge and automated feed- back.
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. 2022a. Fast model editing scale. In International Conference on Learning Representations.
Eric Mitchell, Charles Lin, Antoine Bosselut, Christo- pher D Manning. and Chelsea Finn. 2022b. Memory- based model editing at scale. In Proceedings of the 39th International Conference on Machine Learning. volume 162 of Proceedings of Machine Learning Research, pages 15817-15831, PMLR.
Guangyue Peng, Tao Ge, Si-Qing Chen, Furu Wei, and Houfeng Wang. 2023b. Semiparametric language models are scalable continual learners.
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019. Language models as knowl- edge bases? In Proceedings of the 2019 Confer- ence Empiricul Methods in Natural Language Pro- cessing and the 9th International Joint Conference on Natural Language Processing (EMNLP-UJCNLP), pages 2463 2473, Hong Kong, China. Association for Computational Linguistics.
Reichirr Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju. William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2022. Webgpt: Browser- assisted question- question-answering answering with human feedback.
Ella Neeman, Roee Aharoni, Or Honovich, Leshem Choshen, Idan Szpektor, and Omri Abend. 2022. Disentqa: Disentangling parametric and contextual knowledge with counterfactual question answering.
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. 2023. Mcasuring and narrowing the compositionality gap in language models.
Yasumasa Onoe, Michael J. Q. Zhang, Shankar Padman- abhan, Greg Durrett, and Eunsol Choi. 2023. Can Ims learn new entities from descriptions? challenges propagating injected knowledge.
Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi. Zhiyuan Liu. Maosong Sun, and Jie Zhou. 2023a. Webepm: Interactive web search for chinese long-form ques- tion answering.
OpenAI. 2022. Introducing chatgpt.
OpenAL. 2023a. chatgpt plugins.
Yujia Qin. Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang. Chaojun Xiao, Chi Han. Yi Ren Fung. Yusheng Su. Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li. Ziwei Tang, Jing Yi. Yuzhang Zhu, Zhenning Dai, Lan Yan, Xin Cong.
OpenAl. 2023b. Gpt-4 technical report.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroli Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller,

---

Yaxi Lu, Weilin Zhao, Yuxiang Huang, Junxi Yan. Xu Han, Xian Sun, Dahai Li, Jason Phang. Cheng Yang. Tongshuang Wu, Heng Ji, Zhiyuan Liu, and Maosong Sun. 2023b. Tool learning with foundation models.
Thomas Scialom, Tuhin Chakrabarty, and Smaranda Muresan. 2022. Fine-tuned language models are continual learners. In Proceedings of the 2022 Con- ference on Empirical Methods in Natural Language Processing, pages 6107-6122, Abu Dhabi, United Arab Emirates. Association for Computational Lin- guistics.
Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou, 2022. ELLE: Ef- ficient lifelong pre-training for emerging data. In Findings of the Association for Computational Lin- guistics: ACL 2022, pages 2789-2810, 2810, Dublin, Ire- land. Association for Computational Linguistics.
Sina J. Semnani, Violet Z. Yao, Heidi C. Zhang, and Monica S. Lam. 2023, Wikichat: A few-shot Ilm- based chatbot grounded with wikipedia.
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. 2023. En- hancing
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018. Improving language under- standing by generative pre-training.
large language models with iterative retrieval- -eeerratinnn synergy.
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners.
Yikang Shen, Zheyu Zhang. Tianyou Cao, Shawn Tan, Zhenfang Chen, and Chuang Gan. 2023. Mod- uleformer: Modularity emerges from mixture-of- experts.
Colin Raffll, Noam Shazeer, Adam Roberts, Katherine Lee. Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with unified text-to-text trans- former. Journal of Machine Learning Research, 21:1- 67.
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. 2023a. Large language models can be easily distracted by irrelevant context.
Weijia Shi, Julian Michael, Suchin Gururangan, and Luke Zettlemoyer. 2022. Nearest neighbor zero-shot inference. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. pages 3254-3265, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brownr and Yoav Shoham. 2023. In-context eetrieval-augmentee lan- guage models.
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. How much knowledge can you pack into the param- eters of language model? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5418-5420 Online. Association for Computational Linguistics.
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023b. Replug: Retrieval-augmennte black-box language models.
Kurt Shuster, Jing Xu, Mojtaba Komeili, Da Ju. Eric Michael Smith, Stephen Roller, Megan Ung. Moya Chen, Kushal Arora, Joshua Lane, Morteza Behrooz, William Ngan, Spencer Poff, Naman Goyal, Arthur Szlam, Y-Lan Bourcau, Mclanic Kambadur, and Jason Weston. 2022. Blenderbot 3: a deployed conversational agent that continually learns respon- sibly engage.
Paul Röttger and Janet Pierrehumbert. 2021. Temporal adaptation of BERT and performance on downstream document classification: Insights from social media. In Findings the Association for Computational Linguistics: EMNLP 2021, pages 2400-2412, 2, Punta Cana, Dominican Republic. Association for Compu- tational Linguistics.
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang. Jordan Lee Boyd-Graber, and Lijuan Wang. 2023. Prompting GPT-3 to be reli- able. In The Eleventh International Conference on Learning Representations.
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai. Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szezechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang. Matteo Manica, Sheng Shen. Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang. Tr- ishala Neeraj, Jos Rozen, Abhcesht Sharma, An- drea Santilli, Thibault Fevry, Jason Alan Fries. Ryan Techan, Teven Le Scao, Stella Biderman, Leo Gao. Thomas Wolf, and Alexander M Rush. 2022. Multi- task prompted training enables zero-shot task gener- alization. In International Conference on Learning Representations.
Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mah- davi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cewes, Stephen Pfohl. Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Scharli, Aakanksha Chowdhery, Philip Mansfield, Blaise Aguera y Areas, Dale Web- ster. Greg Corrado, Yossi Matias. Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Ra- jkomar, Joelle Barral, Christopher Semturs, Alan Karthikesalingam, and Vivek Natarajan. 2022, Large language models encode clinical knowledge.

---

Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin. Sergei Popov, and Artem Babenko. 2020. Editable neural networks. In International Conference on Learning Representations.
Xiaokai Wei, Shen Wang. Dejiao Zhang, Parminder Bha- tia, and Andrew Arnold. 2021. Knowledge enhanced pretrained language models: A compreshensive sur- vey.
Niket Tandon, Aman Madaan, Peter Clark, and Yiming Yang. 2022. Learning to repair: Repairing model out- put errors after deployment using a dynamic memory of feedback. In Findlings of the Association for Com- putational Linguisticx: NAACL 2022, pages 339-352, Seattle, United States. Association for Computational Linguistics.
Suhang Wu, Minlong Peng, Yue Chen, Jinsong Su, and Mingming Sun. 2023. Eva-kellm: A new benchmark for evaluating knowledge editing of Ilms.
Yuhuai Wu, Markus Norman Rahe, DeLesley Hutchins, and Christian Szegedy. 2022. Memorizing transform- ers. In International Conference on Learning Repre- sentations.
Kushal Tirumala, Aram Markosyan, Luke Zettlemoyer, and Armen Aghajanyan. 2022. Memorization with-
Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2023. Adaptive chameleon or stubborn sloth: Unraveling the behavior of large language models in knowledge clashes.
overfitting: Analyzing the training dynamics of large language models. Advances in Neural Infor- mation Processing Systems, volume 35, pages 38274- 38290. Curran Associates, Inc.
Yan Xu, Mahdi Namazifar, Devamanyu Hazarika, Aish- warya Padmakumar, Yang Liu, and Dilek Hakkami- Tür. 2023a. Kilm: Knowledge injection into encoder- decoder language models.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux. Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez. Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models.
Yang Xu, Yutai Hou, Wanxiang Che, and Min Zhang. 2023b. Language anisotropic cross- poss-lingual model editing.
Harsh Trivedi. Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. Interleaving retrieval with chain-of-thought reasoning for knowledge- intensive multi-step questions.
Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin. Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. 2023. Mm- react: Prompting chatgpt for multimodal reasoning and action.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, F. ukasz Kaiser, and Illia Polosukhin. 2017. Attention all you need. In Advances in Neural Information Pro- cessing Systems, volume 30. Curran Associates, Inc.
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2023a. React: Synergizing reasoning and acting in language models. In The Eleventh International Conference on Learning Representations.
Jiaan Wang. Yunlong Liang, Zengkui Sun, Yuxuan Cao, and Jiarong Xu. 2023a. Cross-lingual knowledge editing in large language models.
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023b. Editing large language models: Prob- lems, methods, and opportunities.
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, and Ming Zhou. 2021. K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1405-1418, Online. Association for Computational Linguistics.
Da Yin, Li Dong, Hao Cheng, Xiaodong Liu, Kai-Wei Chang, Furu Wei, and Jianfeng Gao. 2022. A survey of Knowledge-intensive nlp with pre-trained language models.
Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao. Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, Qingqing Zhu, Zhenzhu Yang, Adam Nik, Qi Liu, Chenghua Lin, Shi Wang. Ruibo Liu, Wenhu Chen, Ke Xu, Dayiheng Liu. Yike Guo, and Jie Fu. 2023b. Interactive natural language processing.
Zhangyue Yin, Qiushi Sun. Qipeng Guo. Jiawen Wu. Xipeng Qiu, and Xuanjing Huang. 2023. Do large language models know what they don't know?
Pengfei Yu and Heng Ji. 2023. Self information up- date for large language models through mitigating exposure bias.
Zhenyi Wang. Enneng Yang. Li Shen, and Heng Huang. 2023c. A comprehensive survey of forgetting in deep learning beyond continual learning.
Wenhao Yu, Zhihan Zhang, Zhenwen Liang, Meng Jiang, and Ashish Sabharwal. 2023a. Improving lan- guage models via plug-and- -layy retrieval feedback.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022. Chain of thought prompt- ing elicits reasoning in large language models. In Advances in Newral Information Processing Systems.
Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, and Meng Jiang. 2022. A survey of knowledge-enhaneed text generation. ACM Comput. Surr, 54(1 Is).

---

Zichum Yu, Chenyan Xiong, Shi Yu, and Zhiyuan Liu. 2023b. Augmentation-adape retriever improves generalization of language models as generic plug- in.
Re-training. Intuitively, one can regularly train the model from scratch with the latest corpora to align with current world knowledge. However, this naive solution has clear downsides: (1) Re- training is both time and money expensive and environmentally unfriendly (Patterson et al., 2021), especially in the era of LLMs with billions of pa- rameters. For instance, LLaMA-65B was trained for about million GPU-hours and emitted more than hundred tons of carbon (Touvron al., 2023); (2) It unrealistic frequently re-training an LLM in response to the constantly changing world.
Michael J. Q. Zhang and Eunsol Choi. 2023. Mitigating temporal misalignment by discarding outdated facts.
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher De- wan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mi- haylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022. Opt: Open pre- trained transformer language models.
Tianhua Zhang, Hongyin Luo. Yung-Sung Chuang, Wei Fang, Lue Gaitskell, Thomas Hartvigsen, Xixin Wu. Danny Fox, Helen Meng, and James Glass. 2023. Interpretable unified language checking.
Fine-tuning, Another simple approach is to pe- riodically curate small-scale dataset containing desired knowledge we wish the model to add, up- date, or delete, then fine-tune the model on the dataset. Despite being computationally cheaper than re-training, it still falls short in that, with- out constraints, directly fine-tuning the model may have a "butterfly effect" and affect other knowl- edge or skills present in the model (Li et al., 2022), causing degraded generalization (Mitchell et al., 2022a), catastrophic forgetting (Kirkpatrick et al., 2017; Zhu et al., 2020; AlKhamissi et al., 2022), knowledge conflicts (Neeman et al., 2022).
Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing. 2023. Verify-and-edit; A Knowledge-enhanced chain-of-thought framework.
Chaoqi Zhen, Yanlei Shang, Xiangyu Liu, Yifei Li, Yong Chen, and Dell Zhang. 2022. A survey on knowledge-enhancee pre-trained language models.
Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023. Can edit factual knowledge by in-context learning?
Zexuan Zhong, Zhengxuan Wu, Christopher D. Man- ning, Christopher Potts, and Danqi Chen, 2023. Mquake: Assessing knowledge editing in language models via multi-hop questions.
Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2023. Context-faie prompting for large language models.
Constrained Fine-tuning. To solve part of above mentioned issues, Zhu al. (2020) propose to only fine-tune the model on the small-scale modified facts set and add explicit constraints on the model weights so that the model learns to answer the modified facts while keeping the remaining knowl- edge intact. Specifically, they use various norms (Co. L2, and L.o) to prevent the parameters of the fine-tuned model 0' from drifting too far from the original model parameters 0. They further find that fine-tuning only the first and last layers of the Transformer model (Vaswani et al., 2017) results in better adaptation to the modified facts and bet- ter preservation of performance on the unmodified facts. However, the norm based constraint on pa- rameters ignores the highly non-linear nature of LMs and how parameters determine the outputs of the model, making their method potentially unre- liable (De Cao al., 2021). addition, Mitchell et al. (2022a) confirm that constrained fine-tuning generally does not consistently provide edit gener- ality.
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojamapalli, Daliang Li, Felix Yu, and Sanjiv Kumar. 2020. Modifying memories in transformer models.
Fengbin Zhu, Wenqiang Lei, Chao Wang. Jianming Zheng. Soujanya Poria, and Tat-Seng Chua. 2021. Retrieving and reading: A comprehensive survey on open-domain question answering.
Xinyu Zhu, Cheng Yang, Bei Chen. Siheng Li. Jian- Guang Lou, and Yujiu Yang. 2023. Question an- swering as programming for solving time-sensitive questions.
Noah Ziems, Wenhao Yu, Zhihan Zhang, and Meng Jiang. 2023. Large language models built-in au- toregressive search engines.
A Appendix
A.1
Additional Description of Approaches
A.1.1
Naive Approaches
Although more advanced approaches have been proposed, we introduce naive solutions for com-
pleteness in this section.

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Contasic Muskis resprtat
CCC Twillas
cialized training of the original model (De Cao et al., 2021). Moreover, to enforce the constraint that the editable model agrees with the original pre- trained model's s predictions, Sinitsin et al. (2020)*s 's method needs to retain a copy of the original model, which significantly consumes computation memory (Mitchell et al., 2022a). Chen et al. (2023c) also requires training of the original LM, which could be computationally expensive for larger L.Ms. In addition, whether it will influence other irrelevant knowledge in the model remains unknown, making the method potentially unreliable.
Quaohorr Aocending the content, who is the CSC Twitter only provice minntwen
MonMfukk: notero and Twtteer
Figure 5: An example of knowledge conflict of Chat- GPT (OpenAI, 2022). Even if the correct context provided, ChatGPT still favours its internally memo- rized knowledge. The screenshot was taken May 2023 for GPT-3.5 without web browsing.
A.1.2 Knowledge Editing
To facilitate the development of this area, De Cao et al. (2021) formulate three desiderata that an ideal editing method should follow: I Gener- ality: the method should be capable of alter- ing the knowledge of any LM that is not specif- ically trained to be editable (e.g., PaLM, GPT- 4, LLaMA); ② Reliability: the method should only update the targeted knowledge without influ- encing the rest of the knowledge in the LM. For instance, the answer to "Who is the current Prime Minister of Australia?" has changed from "Scott Morrison" to "Anthony Albanese" since 2022, updating the knowledge from "Scott Morrison" " "Anthony Albanese" should not change the knowledge "Argentina won the 2022 World Cup": 3 Consistency (Generalization): after updates, the model predictions should be con- sistent across semantically equivalent inputs (e.g., correctly predicts "Anthony Albanese" " to "Who is the AU PM?"). Beyond updating outdated knowledge, knowledge editing can also delete sen- sitive information for privacy issues eliminate biases in the pre-training corpora.
Hypernetwork Editor. De Cao et al. (2021) can be more efficient than Sinitsin et al. (2020), it does not retain the copy of the original model nor compute higher-order gradients. However, it can only update a single fact rather than multiple facts in a row and fail to edit large models, leading to poor scalability (Mitchell et al., 2022a; Hase et al., 2023b). Mitchell et al. (2022a) improve De Cao et al. (2021)'s 's work and is stable to edit LMs from BERT-base (110M) (Devlin et al., 2019) to T5- XXL (11B) (Raffel et al., 2020). However, when editing multiple knowledge simultaneously, their edit success rate significantly degrades.
Locate and Edit. While simple, Dai et al. (2022) do not ensure reliability on other irrelevant knowl- edge and generalization on semantically equivalent inputs. Despite showing both generalization and specificity, Meng al. (2022a) only edits single f'at a time, making it impractical for large-scale knowledge updating in LLMs. Through casual tracing, Meng et al. (2023) identify and update the critical MLP layers in one go. However, Hase et al. (2023a) argue that the relation between localization and editing may be misleading as they can edit fac- tual knowledge in different locations that are not suggested by casual tracing.
However, not until recently, Onoe et al. (2023); Zhong et al. (2023) show that, after performing knowledge editing, the LLM does not really "learn" the updated knowledge and thus cannot propagate the new knowledge and make further inferences based them. For instance, after learning that "the current PM of Australia is Anthony Albanese", the model might not able to make pre- dictions of "Who is the spouse of the current PM of Australia?".
A.1.3 Continual Learning
While knowledge editing provides a fine-grained control to update specific knowledge in L.LMs, it often requires large amounts of supervised train- ing data to make edits, which is non-trivial to cre- ate (Hartvigsen et al., 2023). In addition, when domain
Metannna Sinitsin et al. (2020), by con- straining the training objective, encodes editability into the parameters of the model itself so that the model is "prepared" for incoming edits. While be- ing effective and no new parameters are required, it does not conform to generality as it requires spe-
LLM needs to quickly acquire knowledge (e.g., legal or medical), such small-scale model edits may not be efficient. Moreover, after multiple parameter patches to a deployed model, its internal knowledge may conflict, leading to un- predictable behaviors (Mitchell et al., 2022a).

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Sharing related goal, continual learning (CL) aims to enable model to learn from continu- ous data stream across time while reducing catas- trophic forgetting of previously acquired knowl- edge (Biesialska et al., 2020). In contrast to knowl- edge editing, CL generally updates models on larger scale and works in long learning sequences with minimal memory overheads (Mitchell al., 2022a). Hence, CL can also be used for deployed models update their knowledge.
A.2 The Complete Taxonomy of Methods
We list the complete taxonomy of methods to align LLMs with the ever-changing world knowledge in Fig.6 and the complete comparison methods in
Table 3. We also compare the characteristics of different methods in Table 2.
Large Scale
No Side Effects
Calegory
Persistent
Knowledge Editing
(.2.1.1)
Continual Learning ($2.1.2)
Retrieval based (82:2)
Table 2:
High-level comparison of characteristics of different approaches.

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No
Black
Category
Representative
LM Angmentation
Training
-box
Naive
Re training tuning
De Cao at al. (2021)
BERT (0. (0.18)
*
auxiliary model
x
MEND (Miichell
(11B)
*
suxiliury
SLAG (Hase al,, 2023b)
BERT (0.
auxiliary model
Knowledge Editing
RECKONING (Chen et sl., 2023c)
GPT-2 (0.1B)
ROME (Meng 2022s)
GPT-J (6B)
Knowledge Neurons (Dai al., 2022)
BERT (0. (B)
MEMIT (Meng 2023)
GPT-NeoX (208)
CanNET (Dong et al., 2022)
T3 (0.7B)
+pazums auxiliary
(Hernandzz al. al., 2023)
DSA (Ko al., 2023)
RoBERTa (0.18)
X
ELLE (Qin 2022)
Seetoory-pprams
CTO (Scialom al., 2022)
TO (3B)
memory
K-Adapter (Wang et 2021)
RoBERTa (0.38)
+params
Gururangan al. (2022)
GPT-2 (0.78)
+params
Contioual Learning
CPT (Ke et 2022)
RoBERTa (0.1B)
+params
KILM (Xa 20234)
BART
CaMeLS (Hu ot al., 2023)
GPI22 (1,58)
auxiliary model
memory
ScMem (Pong al., 2023b)
GPT-2 (0.78)
+mxxillary model
Cl-plugin
2022a)
TS (0 (0)78)
+paras
[uang et al. (2023)
BERT (0.IB)
+parums
(lartvigsen al., 2023)
(0.06B)
memory
ADP
ANN-LM (Khandelwal et 2020)
(Raevski and Auli, 2019) (0.28)
x
AdaptRet (He al., 2021a)
ADP (0.28)
+auxiliary model
menuoy
x
RetoMaton (Alon al., 2022)
ADP (0.20)
memory
x
x
+auxiliary graph
Memory -enhanced
ANN-prompt (Shi al., 2022)
GPT-2 (0.8B)
memmory
*
+constraint
PRNet (Tandon
T5
+auxiliary mudel
memory
MemPrompe (Madnan al., 2022)
(1753))75B))
memory retriever
et 2022)
(IJR)
memory-netrievs reeriieer
SERAC (Mitchell 2022b)
TS (0:78)
memory +auxiliary model
MeLLo (Zhong et al., 2023)
GPT.3.5 (175B)
memory +retrieeer
(C-Retrieval
2023)
GPT33.5
retriever retriovez-eeankkr
IC-RALM (Ram ot al., 2023)
OPT (668)
(Zheng
(175B)
AAR (Yu al., 2023b)
GPF335 (175B)
retriever
RePlug (Shi et al., 2023b)
GPT:3 (1758)
retriever
xiv
IRCsT (Trivedi
2022)
GPT:3.5
reltiever
RARR (Gso et al., 2023)
PaLM (540B)
search engine
+auxillary model
Retrieval enhunced
RR (He et al., 2022)
GPT-3.5 (1755)
+auxiliary model
(Yu 2023a)
GPT:3.5
reltiever
DecomP (Khor ol., 2023)
GPT:3.5 (175R)
retriever
ReAct (Yao al., 2023g)
PaLM (540B)
search engine
Self-Ask (Press 2023)
GPT-3 (175B)
FLARE (Jiang et 2023)
(1753)((15B))
retriever/scareh engine
(Khattab al.,
ART (Parunjape et al., 2023)
GPT-3.5(1755)
various inols
Iter-Retiinn
2023)
GPT33.5 (1758)
retriever
Verify and Edit (Zhao al., 2023)
GPT33.5 (175B)
retrieverkearcch engine
Lazaridou (2022)
Gupher
engine
CRTTIC (Gou at.l. 2023)
(1753)
86
various inols
Internel <nhanced
Rewriter (Ma et al., 2023)
5PF:3.5 (1758)
search engine
Chameleun (La 2023)
4PT-4
various tools
CharGPT Plugins (OpenAl, 2023a)
3PT.3.5 (175B)
various tnols
Table 3: Comparison between representative methods. means the parameters of the original LM are modified, while & means they are unchanged; Augmentation means additional components used: No Training indicates the method does not require additional training; Black-box refers to whether the method suits non-publicly available models (e.g., g., no model architecture, parameters, activations, or gradients are available). Note that we only list the largest size model used in the paper due space limitations.

---

Re-training
cuning
Meta learning
Editable Traning (Sinitsin et al., 2029), RECRONING (Chen 2023))
Hyperetwork
Knowleggeliiior
MIND (Mitchell 2022a). SLAG (lase Distilation (Pudmansibun
REMEDS (Heruuadzz
Implicit ($2.1)
Kaowledge Ediiiing
Knowledge Neurons 2022),
2022a),
2023), MEMITes (Guutaa (2025),
20236), (20226))
and edit
Zhung.
Exs-KELLM (Wa 2023), RipoleEdit ol., 2023), Wang (3023a), al. (2022b), IKE i2hess et ill, 2023)
Other
Kegularizatoo: lased
RezAdbenn (Chmm et al., 2020), DSA (Ke al,, 2023)
Replay-hoood -oooe
Mis-Reviow (Hl 2001)), ELLE (Qin et 2022), CTO (Scinlom
2022)
Continal Pro-tiiinn
K-Adapter DEMEE--DAPT (Cuminggnn 2023, Lifelong
LoKA
BELLI CPT
Aschitectual-
2023a), Modulet
LLMs ever-clanging knowledge
Continal Lesming
Tampocal-LM (Dhingra 2022), Lifelong Pie-crinnin 2022) CKL UJsg al, 202200, YemporalWik Usng et at. 2022a), TopicPrelis (Lee et al,, 20020), KILM (Xu et al., 2025ax ScMem (Ping et al. 20235), S (Ilo en all, 2023), Ya and In (2025), Cupta al (20236)
al.,
Otlic
Comineal Knowledge
CMR (1 2022), Cl-plagin (1ae al., 2022a), Tranfforme-Puchher (Hunng al., 2023), GRACE (Hutvigses 2023)
(Khandeleal 2020,, AdapcRet (He al., AdugiCect al., 2022, RetoMaton (Alon sl., 3022), Rhundogj (2022) ANN-ppoopp (Sha al., 2022), SeMem (Peng al., 20236)
Corpus or Docament
Memory- enhunced
Belief Bank (Kaenar al. 2021), FRNer (Tundon et al, 2022), MemPronye (Midaas al., 2002), TeeehMMe (Dalss Mishi et all, 2022), SERAC (Michell et al., 1012h) Mal.a (Ziong ol., 2023)
Feedhack Correctinns
IC (C-Retrieval (Si 2023), IC-RAIM (Rum al., 2023), AAR 20238) IKE (Zhngg 200),, Adaptive Retrivaal (Mallen 2025), Rellug (Shi al. 20236)
Single-Stnge
Explicit (52.2)
Ratricnal enhunced
IRCsT (Thivedi
Self-Ask RARR (Gao 2023),
ReFoed 20250) MutTTol-Coo 20236), QAaP (2ho all, 2003),
(He
2023a), ART (Piannjaee ahs al,, 2003) L.M-Augmenter (Pang
Stage
(Rang al., 2023), DSP (Khamah et al, 2003), Tic-ReiCen (Shin et al, 2023), Verif-andd Edit (Zhno el al., 2023), CRTTIC (Gou el all 2023), WhiCChi cSomaani all, 2023), LLM 2023), Knowledge Solver (Fong 2023)
Rewntter (Ma
Internet-Fowbbt (Laxaiiou et al. 2002), TLM-URI (Zimms al., 2023), ReAct (Yan al. 2033)) Self-Aak (Fress MM-REACT (Ying (Paraniape 2023),
TaskMamix CRITIC FLARE dlingg 2025), LangChain (Chase, 2022), CharGPT Flugits (UpenAL. 2023a)
Clamekonn RARR (Gao
2022,
Figure 6: Taxonomy of methods to align LLMs with the ever-changing world knowledge. Implicit means the approaches seek to directly alter the knowledge stored in LLMs (e.g., parameters) ($2.1), 1), while Explicit means more often incorporating external resources to override internal knowledge (e.g., search engine) ($2.2).


Reviews:
[{"review_id": "25t995nAnM", "reviewer": "Reviewer_j2Rw", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 1, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer j2Rw,\n\nWe appreciate your valuable time, review and suggestions. We want to address your concerns in detail as follows.\n\n> Q1. Not clear how the papers are selected (i.e., survey methodology) \n\n**A1:** Due to the limited space in the paper's main body, we have briefly described how we selected papers in the Limitations Section (L642). Specifically, we mainly review cutting-edge papers from the top conferences, including ACL, EMNLP, NAACL, TACL, NeurIPS, ICML, ICLR, and some of the most recent papers from arXiv. We find papers from these venues via keyword searching from the paper title and abstract. Then, we manually filter out irrelevant papers and categorize the remaining into different approaches.\n\n> Q2. and the search timeframe (last N years?).\n\n**A2:** \nAs mentioned in the Limitations Section (L647), we review papers from the **three** recent years (2021-2023), mostly in 2022 and 2023. We set the timeframe as three years because: \n1. the concept of “large language models” arose within three years\n2. much work has been proposed in aligning large language models with world knowledge, especially after the release of the ChatGPT\n\nWe want to review the recent advances in the LLMs era.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Solid and comprehensive work.", "reasons_to_reject": "Not clear how the papers are selected (i.e., survey methodology) and the search timeframe (last N years?).\n\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper surveys techniques related to LLM alignment over changing information that does not require training from scratch. The paper is comprehensive in nature.", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "R4iT0aKoMA", "reviewer": "Reviewer_PyMv", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer PyMv,\n\nWe appreciate your valuable time, review and suggestions. We want to address your concerns in detail as follows.\n\n> Q1. I would have liked to see a quantitative studyin the paper, too.\n\n**A1:** \nAs discussed in the Limitations Section (L671), we acknowledge that we do not have quantitative comparisons of different approaches through experiments but only empirical analysis and summarization of existing work. We also agree that implementing different approaches on the same LLM would make comparing various methods easier and more straightforward. \n\nHowever, this **unified** evaluation of different categories is **not trivial** -- existing work evaluates their methods in the same category, and there are no unified suitable benchmarks, datasets, and settings for fair comparison of different categories. For example, knowledge editing methods typically evaluate the fact-checking tasks to verify that knowledge has been updated, while continual pre-training usually evaluates both the source and target domains. It might be unfair to say which category is more effective without a unified evaluation setting. \n\nThat's why we provide a qualitative analysis of different categories in this survey to help choose suitable approaches under different scenarios.  In fact, as we discussed in Section 4 (L607), quantitative evaluation (benchmarks) is a challenging and interesting future direction to fairly compare methods of different categories to align LLMs with updated world knowledge. We will leave quantitative comparisons and analysis as future work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "+ Exhaustive, complete, and very useful\n+ Provides a notion of what methods may make more sense in what situations\n+ Includes a perspective of future trends and challenges. ", "reasons_to_reject": "- I would have liked to see a quantitative studyin the paper, too.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper presents an extensive and detailed compendium of the different methods developed in recent years to align LLMs with updated world knowledge. The paper proposes a taxanomy to structure this study where the different methods are divided in two main faimilies: implicit, where the approaches seek to directly alter the internal representations of the LLM, and explicit, where external resources are incorporated to override internal knowledge during inference.\n\nIn addition to the exhaustiveness of this work, I would also highlight how the authors put in context each of the different approches covered in the study, providing a perspective of what methods may make more sense in what situations, and offering a perspective of future trends and challenges. \n\nThe only criticism I have is that I would have liked to see a quantitative study that allowed to understand the effectiveness of the different methods. This could have been done, e.g. by selecting those LLMs that have been updated in past work through different approaches and showing the relaitve impact of each such approach on the same LLM. ", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "fYig7bQzuL", "reviewer": "Reviewer_CDPY", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer CDPY,\n\nWe appreciate your valuable time, review and suggestions. We want to address your concerns in detail as follows.\n\n> Q1. Missing References: Pan et al. (2023)\n\n**A1:** Thanks for pointing out this related survey. However, Pan et al. (2023) was first released on 20 June on arXiv, three days before the EMNLP submission deadline and after our paper writing. It is regarded as **contemporaneous work** according to the policy. Having said that, we will cite this paper and compare it when we can modify our paper. \n\n> Q2. The authors leave out a fast area of material and a key type of explicit knowledge. This can be called \"keeping LLMs up to date with structural knowledge\".\n\n**A2:** \nThanks for your comments. We acknowledge that structural knowledge such as knowledge graphs (KGs) can broadly be categorised as explicit knowledge, which can be retrieved from external to adapt LLMs to new world knowledge.\n\nHowever, please note that we consider the issue that LLMs can be outdated after **deployment/training**, and how to align trained LLMs to the **ever-changing** world knowledge. We focus on **dynamic**, not static, as stated in multiple places in our paper, e.g. L007, L332, L446, L1616, L1622.\n\nAfter creation, KG is static and can still be outdated [Ji et al. (2021)]. New information or discoveries not yet incorporated into the graph may lead to potentially outdated knowledge. *Therefore, simply incorporating structural knowledge (i.e., KG) into LLM does not solve our problem.*\n\nUnfortunately, mainstream research focuses on static KGs [Ji et al. (2021), Page 17, Section F], and there is little work on using KGs to update new knowledge for LLMs to reflect the most recent or unseen facts and knowledge. Having said that, if any applicable approaches are missing, we are delighted to add them.\n\nOn the other hand,  our survey scope is to align already trained/deployed LLMs (such as Llama, GPT4) with the ever-changing world knowledge **without retraining from scratch**. From our best understanding, most of the existing KG-enhanced LLM approaches require special pre-training objectives to integrate KGs into LLMs, which need to be retrained from scratch and can **not** be applied to a deployed LLM [Pan et al. (2023), Section 4.1]. For example, as summarized in Pan et al. (2023) Table 2, both REALM[1] and RAG[2] require joint training of the retriever and the model (we have discussed REALM and RAG in our paper as well at L332).\n\n\"Keeping LLMs up to date with structural knowledge\" is a good idea and could be another paper. We will add discussion in the revision. Thanks. \n\nJi et al. (2021): A Survey on Knowledge Graphs: Representation, Acquisition and Applications, 2021\\\nPan et al. (2023): Unifying Large Language Models and Knowledge Graphs: A Roadmap, 2023\\\n[1] REALM: Retrieval-Augmented Language Model Pre-Training, 2020\\\n[2] RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020\n\n> Q3. This type of assuring up-to-dateness is of high importance for LLM applications. And there are existing research reviews that show approaches to leverage structural data for improving LLMs, see esp. the paper from Pan et al. (2023)\n\n**A3:** \nWe agree that structural data is useful for improving LLMs and has a lot of potential. However, for the broader structural knowledge, including KGs and other types, there is **rare** work on incorporating structural knowledge such as KG into LLMs for updating data and **without retraining**. In Pan et al. (2023), most works for improving LLMs are about overcoming hallucinations, improving interpretability, and accessing domain-specific knowledge. (We just mentioned KG-enhanced LLMs here. Other parts in their paper, such as LLM-augmented KGs and Synergized LLMs + KGs, are not relevant.). The current *“leverage structural data for improving LLMs”* does not fully align with our scope. In addition, we have compared our work with related knowledge-enhanced LM surveys in the Appendix (L1616). \n\nWe will add future work to discuss it. If the KGs can be easily updated with recent and new knowledge, it can provide a way to work with LLMs for new knowledge. Again, if any applicable approaches are missing, we would be delighted to add them.\n\n> Q4. As the authors state, they cannot take into account all references. But looking at the references section of this proposed paper, and the reference section of Pan et al. (2023), one can see that the authors are not aware of a huge body of work related to explicit structural knowledge.\n\n**A4:**  \nThere are two reasons for having different reference sections with Pan et al. (2023).\n\n**First, our survey scope is different from Pan et al. (2023).**\nThere may be a misunderstanding about our scope. The scope of Pan et al. (2023) is about *KG-enhanced LLMs*, *LLM-augmented KGs*, and *Synergized LLMs + KGs*. Only *KG-enhanced LLMs* is relevant to us because we aim to improve LLMs instead of improving KGs. In the KG-enhanced LLMs, they discuss hallucinations, interpretability, and accessing domain-specific knowledge. They do not mention ever-changing knowledge. As mentioned in the abstract and in the above answers, our scope targets the **ever-changing** world knowledge **without re-training**. \n\n**Second, our surveyed time range differs from Pan et al. (2023).**\nAs mentioned in the Limitations Section (L647), we review papers from the **three** recent years (2021-2023), mostly in 2022 and 2023. In contrast, Pan et al. (2023) mainly review papers before 2022. For KG-enhanced LLM methods, as shown in Table 2 in their paper, most of the methods are before 2021, not to mention these methods do not apply to trained models.\n\nDue to the above reasons, our references section does not entirely overlap with Pan et al. (2023), but this does not mean we are unaware of the work related to explicit structural knowledge.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The authors provide a useful overview for a sub area of research: keeping LLMs up to date with text-based knowledge sources.", "reasons_to_reject": "The authors leave out a fast area of material and a key type of explicit knowledge. This can be called \"keeping LLMs up to date with structural knowledge\".\n\nThis type of assuring up-to-dateness is of high importance for LLM applications. And there are existing research reviews that show approaches to leverage structural data for improving LLMs, see esp. the paper from Pan et al. (2023)\nhttps://arxiv.org/pdf/2306.08302.pdf\n\nAs the authors state, they cannot take into account all references. But looking at the references section of this proposed paper, and the reference section of Pan et al. (2023), one can see that the authors are not aware of a huge body of work related to explicit structural knowledge.\n\nFor this reason, I am deeply concerned about the paper in its current state. It would need a thorough re-write to take into account the work on structural knowledge for improving LLMs. \n\nSuch a rewrite could help a lot in closing the gap between research communities. But I am not sure if the authors can do this as part of an updated version of the paper, or rather would want to create a new paper on its own.\n\nI have read the authors response from the rebuttal. Here are my responses.\n- I agree that the timing for referencing Pan et al. in the initial submission does not work. Thank you for considering referencing the paper in the final submission!\n- I agree that updating a KG is a challenge. However, I would say that Pan et al. and the combination of LLM+KG discussed in Pan et al., the part of \"KG enhanced LLMs\", is only about static knowledge.\n- I disagree with the statement that \"KG enhanced LLMs\" is only about finetuning. In the rebuttal, the authors mention retrieval augmented generation as an example for updating an LLM with finetuning. As of today, there are many examples even in industry practice that demonstrate how to realise RAG with knowledge graphs. You can find one related blog post at https://neo4j.com/developer-blog/fine-tuning-retrieval-augmented-generation/ .\n- A method which has more and more attention to implement RAG is the use of vector data bases. Again in industry practices, there is guidance on how this can be realised with KGs, and what the benefits of both approaches are, see https://neo4j.com/blog/knowledge-graph-vs-vectordb-for-retrieval-augmented-generation/. \n\nI do not have a scientific reference at hand, but hope that these examples from industry are relevant for you.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper analysis existing research on how large language models can take evolving world knowledge into account.\n\nThe authors create a taxonomy of approaches, with the broadest categorisation is \"implicit\" versus \"explicit\".\n\nThe authors restrict themselves to text-based knowledge.", "excitement": 4, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "As mentioned above, Pan et al. (2023) \n\nhttps://arxiv.org/pdf/2306.08302.pdf\n\nIs a crucial source about research on updating LLMs with explicit knowledge."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The main conclusions of the reviews and the post-rebuttal discussions:
- 3/ 3 reviewers consider the paper sound (scores 3, 4, 3)
- 3/ 3 reviewers find the paper exciting (scores 4, 4, 4)

Front reading the rebuttal and seeing the scores above, I find that the reviewers consider strong points for soundness the following:
- the paper provides a comprehensive survey on LLM alignment over changing information that does not require training from scratch
One concern is no quantitative comparisons of different approaches through experiments but only empirical analysis and summarization of existing work and it is addressed in the limitations section of the paper.
Concerns from R3 about inclusion of more citations is clarified in the rebuttal.

All reviewers find the paper very exciting!
Decision: Accept-Main