Improving Sequential Model Editing with Fact Retrieval
Authors: XiaoQi Han
Ru Li
Hongye Tan
Wang Yuanlong
Qinghua Chai
Jeff Z. Pan
Venue: EMNLP 2023 Findings (2023)
Keywords: Model Editing; Sequential Model Editing; Pre-trained Language model
TL;DR: A fact-aware retrieval-based editing method achieves efficient, stable, and scalable sequential model editing.

Abstract: The task of sequential model editing is to fix erroneous knowledge in Pre-trained Language Models (PLMs)  efficiently, precisely and continuously. Although existing methods can deal with a small number of modifications,   these methods experience a performance decline or require additional annotated data, when   the number of edits increases. 

In this paper, we propose a $\textbf{R}$etrieval $\textbf{A}$ugmented $\textbf{S}$equential Model $\textbf{E}$diting framework ($\textbf{RASE}$)  that leverages factual information to enhance editing generalization and to guide the identification of edits by retrieving related facts from the fact-patch memory we constructed.
Our main findings are: 
(i)  State-of-the-art models can hardly correct massive mistakes stably and efficiently;
(ii) Even if we scale up to thousands of edits, RASE can significantly enhance editing generalization and maintain consistent performance and efficiency;
(iii) RASE can edit large-scale PLMs and increase the performance of different editors.
 Moreover, it can integrate with ChatGPT and further improve performance. Our code and data are available at: https://github.com/sev777/RASE.

Improving Sequential Model Editing with Fact Retrieval
Xiaoqi Han Ru Li + Hongye Tan + Yuanlong Wang" + Qinghua Chai + Jeff' Z. Pan School of Computer and Information Technology, Shanxi University, China ILCC, School of Informatics. University of Edinburgh, UK 2021 0e01111440000mmaa......m edu. cn, {liru, yla charles eesxu. edu. cn rew.rngerrrreeeeeettttaaeee org/j pan/
Abstract
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The task of sequential model editing is to fix erroneous knowledge in Pre-trained Language Models (PLMs) efficiently, precisely and con- tinuously. Although existing methods can deal with small number of modifications, these methods experience a performance decline or require additional annotated data, when the number of edits increases.
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In this paper. we propose a Retrieval Augmented Sequential Model Editing frame- work (RASE) that leverages factual informa- tion to enhance editing generalization and to guide the identification of edits by retrieving related facts from the fact-patch memory constructed. Our main findings are: (i) State-of- the-art models hardly correct massive mis- takes stably and efficiently; (ii) Even if we scale up to thousands of edits, RASE can signifi- cantly enhance editing generalization and main- tain consistent performance and efficiency; (iii) RASE can edit large-scale PLMs and increase the performance of different editors. Moreover, it can integrate with ChatGPT and further im- prove performance. Our code and data are avail- able at: https: //gi thub. com/sev777 sev777 sev777/RASE.
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Comparison of RASE with other methods.
Other methods (a) involve continuous modification of PLM's parameters. However, the efficiency of modifica- tion decreases as the number of edits grows, resulting in poor performance for SME. Our method (b) leverages an fact retrieval framework, guaranteeing consistent mod- ification efficiency regardless of the number of edits. This improvement enhances both the scalability and per- formance of SME. Furthermore, by leveraging factual information relevant editing, our retrieval method bet- ter adapts to SME scenarios.
1 Introduction
et al., 2019). Prior works have proposed Model Editing (De Cao et al., 2021), enabling fast, data- efficient PLMs updates without damaging model performance on unmodified data. These methods focus on simultaneously modifying edits; however, the errors in PLMs are unpredictable and pervasive, thus correcting them at once is impractical.
Pre-trained Language models (PLMs) are trained on massive amounts of texts, encoding knowledge in their parameters, and having remarkably suc- ceeded in knowledge-driven tasks such as question answering (Kwiatkowski et al., 2019; Chen al., 2021, 2022; Hu et al., 2023) and reasoning (Mi- haylov et al., 2018; He et al., 2023). Such paramet- ric knowledge complements (Pan et al., 2023) the explicit and structured knowledge in widely used knowledge graphs (Pan et al., 2017a.b).
To this end, Sequential Model Editing (SME) (Huang et al., 2023) has been proposed to fix a se- ries of bugs in order while maintaining the previous edits and performance across unmodified data. This is challenging task, since, for highly non-linear model, even slight perturbations might significantly alter the model's output. As shown in Figure. (a), prior works on SME make modifications by ei- ther directly modifying the parameters of the lan-
As Pre-trained Language models are deployed widely, the need to keep their parametric knowl- edge correct and up-to-date without massive retrain- ing costs becomes increasingly important (Sinitsin
"Contact Authors

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guage model (Meng et al., 2022, 2023) or continu- ously adding parameters to the model (Huang et al., 2023), and they use additional storage to maintain the model's s performance on unmodified data (Lo- cality). They have shown promise only to deal with a small number of modifications, and suffer from insufficient expressiveness: 1) As the number of edits increases, the model's parameters undergo significant changes, or the number of parameters becomes large, resulting forgetting previous ed- its or unmodified data and gradually increasing the cost of modifications. 2) In SME, the edits may clash with pre-sampled data stored to preserve Lo- cality. Thus, the cost of maintaining memory will increase when dealing with more edits.
fact-patch memory, and using a query mod- ule classify the edits by retrieving related facts from the memory and achieving efficient, stable, and expansible SME.
Our method can be integrated with other fac- tual knowledge editors, cnhancing the general- ization capability of each editor by leveraging facts related to the edits, and achieving more reliable modifications.
Experiments show that RASE can support large PLMs for stable and efficient continu- ous editing. Moreover, utilize ChatGPT to re-rank retrieval results, further enhancing the accuracy of fact retrieval and identify modi- fied and unmodified data more accurately, thus maintaining the model's 's performance on the unedited data better.
One feasible way to address the abovementioned issue is the memory-based method, which uses an edit-history memory while keeping the origi- nal model frozen. However, the existing method (Mitchell et al., 2022b) primarily focuses on batch editing, assuming that all editing data is known and utilizing the data to train models for data-type identification and modification, which makes it in- efficient when handling Sequential Model Editing.
Background
2.1 Model Editing
The task of Model Editing (ME) (De Cao et al., 2021) is to intervene the target model's behaviour on specific example while preventing the model from forgetting unmodified data. Previous research can be classified into two categories: Specification-bast
To fully leverage the advantages of the memory- based method, we propose RASE, a Retrieval Augmented Sequential Model Editing framework, (cf.Figure. (b)) stores edits with their fact descrip- tion and parameter shift on PLMs in memory and uses query module to retrieve from them to apply each modification individually. With this frame- work, can simplify complex continuous modi- fications by breaking them down into multiple in- dividual edits, and the SME can be decomposed into the following two sub-tasks: 1) Edits clas- sification: determining whether the input needs modification. We propose a fact-aware contrastive model to identify edits without pre- pre-training training on any annotated datasets by learning the sentence and fact embedding in self-supervised way. 2) Editing: de- termining how efficiently modify each edit. We enhance the generalization capabilities of existing editors by incorporating factual information with the editing data.
Methods These methods
(Zhu al., 2020; De Cao et al., 2021; Mitchell et al., 2022a: Meng et al., 2022. 2023; Han et al., 2023) fix the bugs through locating and modifying specific parameters in PLMs. However, a minimal change in parameter space may produce com- pletely different output for many data, which may leave post-edit model potentially unreliable. Addition-Baan Based Methods Instead of directly modifying the parameters of PLMs, the Addition- Based Methods (Dong al., 2022: Mitchell et al., 2022b; Huang al., 2023) utilize an additional module to apply the edits while keeping the pa- rameters of the PLMs fixed. They allow better preservation of the original language model's per- formance and exhibits excellent scalability.
However, both types of methods focus on batch editing. In practice, language model errors often require timely and sustainable correction, and the to-be-modified data unknown.
Experimental results on fact-checking and ques- tion answering tasks indicate that RASE can rectify a series of mistakes (up to thousands) while retain- ing the model's performance unmodified data. Our contributions include: We propose RASE, a retrieval augmented knowledge editing framework, constructing a
2.2 Sequential Model Editing
The task of Sequential Model Editing (SME) (Huang et al., 2023) is to fix a series mis- takes of a target model as soon as they appear.

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Figure 2: Illustration of RASE. Figure (a) illustrates the sentence encoder and fact encoder trained in a self- supervised way. Figure (b) shows the memory construction process. We use the facts embeddings as the key and the parameter shift for edits as the value. Figure (c) presents the retrieval framework, where the query module retrieves information from the memory for each input. Based on retrieval results, we make appropriate modifications to the PLM, correcting the data that needs to be modified.
Formally, given a PLM F(.) and an edit stream D ((1133e)) ) (I2, Vrz), ............ (I,,, } with n edit inputs, the task is to effectively correct the output of F(z)) when F(x;) # Uee. while maintaining accurate predictions for previous edits and unmod- ified data. We denote & as the editing function, after processing the i-th input, the F.(.) can be represented as:
sponding changes to the PLMs based on the re- trieval results, the performance the retriever can ensure the reliability of the model's 's locality.
3 Approach
Figure 2 shows the overview of RASE. In a nut- shell, we first train fact encoder and sentence encoder in self-supervised way to maximize the similarity between sentences and their correspond- ing fact description. After applying each edit suc- cessful, we encode the corresponding fact as a key (Section 3.1.1), treat the required parameter shift as a value (Section 3.1.2), and store the (key, value) pair. During the evaluation, we compare given in- put with the keys in the memory (Section 3.2). a key is found, it indicates that the data needs modifi- cation, and the value will be applied to the language model to complete the editing. Otherwise, the input is simply passed the original model. We illus- trate our approach by introducing the facts-patch memory's 's construction and usage.
FO) Fi-(()) ((FFL1(), Vw:)
Fi:1(x;) = Yxst
Fi;)( A Yec. (1)
Compared with ME, the desiderata for the SME method are as follow: (I) Reliability, the editor is supposed to successfully edit data sequentially, and the post-edit model should retain the output for pre- vious edits after editing every edit; (2) Generality, the editor should generalize over the equivalent in- puts about the edits; (3) Locality, the editor should retain its accuracy unmodified data.
Existing editing methods (Meng et al., 2022; Mitchell et al., 2022b; Huang et al., 2023) in Se- quential Model Editing (SME) still exhibit limi- tations regarding the generalization and the scale of edits, moreover, due to their direct modify the parameters of the PLMs, the performance of local- ity may also become unreliable. To address it, propose retrieval-augmmente editing framework, leveraging factual information associated with the edits as guidance and enhancing the generalization capability the cditor, leading to stable and scale sequential editing, furthermore, since we will keep the original PLMs frozen, and only make corre-
3.1 The facts-patch memory
The memory M = (K, V) contains facts represen- tation K and the edit operation V, with cach key k; mapping to one value v;. When input has the highest similarity any key in memory, we can consider that the input needs modification. There- fore, we only need learn the embeddings that can maximize the similarity between the input and its relevant facts in order to achieve data classification for editing without relying on labeled data.
3.1.1 Construction Key
'The inputs with the same meaning can differ in natural language expressions called equivalent inputs; e.g., 'Michael Jordan was horn in ?" vs. 'The hirthplace of Michael Jordan is 2".
We use the key K (ko: ki,..., k..... to deter- mine whether the input needs editing during eval- uation. Each k; is generated by the fact encoder

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viewed as the sentence embedding E, of L82s. Contrastive Loss between Sentence and Fact (L$2F) pulls the embeddings of sentences and their related fact close while keeping unrelated facts apart. To ensure that positive samples' similarity surpasses predefined threshold, we use margin- based similarity to better discriminate distinct but semantically similar pairs by increase the similarity between negative samples and decrease the simi- larity between positive samples. In summary, our total loss is:
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Figure 3: Fact-aware contrastive learning method: we represent the inputs (in blue), their equivalents (in green), and corresponding facts (in orange) as positives, while representing other facts and sentences (in gray) as negatives. Our goal is to pull together the positive pairs and push away the negative pairs by training two Encoders.
Lei = X1LF2F + A2L + A3Ls22, (2)
where A; is hyperparameter. See more details Appendix A. Notably, enable to enhance the gener- alization of editor, the factual description is stored simultaneously with the fact embedding in M with- out labels. e.g. for the sentence "IPhone 14 was created by?", the factual description is: "TPhone 14 iPhone 14 is developed by II IPhone 14 was created by?" During the evaluation, each input will be encoded by Enc,(-) and computed with K determine whether a modification is required.
(:ee()) which aims to maximize the similarity be- tween ky and the relevant sentences. Among many possible implementations of the encoder, a straight- forward way is to utilize a language model or an existing retriever Izacard et al. (2022a) to encode the data. However, the representation is ineffective for cases where sentences and factual descriptions are distinct but semantically similar, especially for some sentences where only one word different.
3.1.2 Construction of Value iz
Thus, we propose a fact-aware contrastive learn- ing method to training the encoder. Specifically, we training two Encoders E-)cy() and E-c,(-) to get the fact embeddings F and sentence embeddings Eg respectively, and the goal is to maximize the similarity between the embeddings of sentences and factual information. As shown in Figure for the fact "<Iphone 14, is developed by, Apple Inc.>", we view "Iphone 14 is developed by Apple Inc." as a factual description and use Enncy) :) to encode it. Then, we use Enca(.) to encode its corresponding sentences as positive pairs (indicated in green and blue.) and unrelated sentences (indicated in grey.) negative pairs. We trained Ency() (-) and Enc,() (-) using the following three contrastive losses: Facts Contrastive Loss (L-r2r). We use Lyzr to maximize the similarity between the fact embed- ding E and its positive embedding ExPo (dp means it is generated by dropout noise within transformer layers), and minimize the similarity between the fact and other facts. In this way, fact embedding is learned in self-supervised way.
The value v; for each key ky represents the edit op- eration for the edit Te. In this paper, we choose an addition-based editor, T-Patch (Huang et al., 2023) and a specification-based editor ROME (Meng et al., 2022), as the base editor due to their effi- ciency and the ability to continue editing.
T-Patch (Huang al., 2023) only requires train- ing certain number of neurons for each edit and inserts them at a designated layer of the Trans- former. contrast to Huang et al. (2023), we does not require additional memory to store training data to satisfy locality. Moreover, we concatenate the factual description with the input a prompt to im- prove the generalization of the editor. For T-Patch, the value 0y is the extra neurons.
ROME (Meng et al., 2022) locates the knowl- edge required modification with a key-value pair in one of the middle MLP layers and modifies the corresponding knowledge by directly updating the key-value pair. For ROME, we use factual descrip- tion prompt to improve the generalization of the editing process. The value v; consists of vector value us. lookup key and a shared matrix C. For both two editors, we also store the edits simulta- neously with the i2 in M to help to identify the data type. The details of these two editors are in
Sentences Contrastive Loss (Ls2s). Ls2s and LFFF are computed in the same way. Since one fact corresponds to multiple positive sentences, so the average representation of these sentences is

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Appendix B. We maintain the M during training in real-time. See more details in Appendix C.
We use the same data splits for both datasets as Huang et al. (2023), specifically we employ the original validate as Dtest and the original Drrain is split into an edit set Dedia a new training set IN (ruiin and a new validation Deat- We denote the initial model as fo which is trained on Draiin and validated on Deat. The model fo is sequentially edited on Deda. Supposing that there are T total mistakes in Dedtt I(-) represents the indicator func- tion. After editing the t-th edit example (rr, F2.). we obtain a post-edit model fi, and we use the following metries to evaluate our method:
3.2 The usage of memory
In this section, we introduce how to utilize memory MM to identify which data requires modification and achieve sequential model editing.
For each data, after we obtain the embedding of the input using the sentence encoder H = Enes(z), we match the H with the keys K in the memory M by the score function:
score (o, K),
(3)
Success Rate (SR): For each edit, we test if the post-edit model fi produces the desired prediction. Edit Retain Rate (ER): After editing T edits, we evaluate how many past edits are retained by the final model fr.
where Cos(.) is the cosine similarity, K means the fact embedding matrix in memory M.
We then select the Top-K K scores from score and calculate the following conditions: (1) MAX: the highest similarity score; (2) DIFF: the difference between the top-1 and top-2 score; (3) STD: the standard deviation of the Top-K scores. When the following conditions met, the data is the edit:
Generalization Rate (GR): After editing T ed- its, we evaluate if the post-edit model Jr is edited successfully on the equivalents of the edits in DDaat Training Retain Rate (TrainR): After editing T edits, we compare the performances of the post-edit model fr and the initial model fo on sub-dataset ir which is randomly sampled from ID Wirainn
ED= L (MAXX>)) OR ((DIFD)A)) AND (STD>+))): (4)
where t, tu and fo are the thresholds of the similar score, difference score and standard deviation value respectively.
Test Retain Rate (TestR): After editing T ed- its, we compare the performances of the post-edit model fr and the initial model fo on original vali- date dataset Dest
In lEq. 4, for data with maximum score less than f, we make judgments based on the distribution of the score, such as STD and DIFF. However, it is difficult to determine whether they need to be modified based on their distribution when they are similar but different. Therefore, we propose the two-way score to enhance the differentiation between data points and improve the identification of edits.
4.2 Baseline and Experiment Details
We compare RASE with the following knowledge editors: (1) the specification-base methods: Fine- Tuning (FT) (Zhu al., 2020), FT with KL diver- gence (FT+KL), MEND (Mitchell et al., 2022a), ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023), and (2) the addition-based meth- ods: SERA (Mitchell et al., 2022b) and T-Patcher (Huang et al., 2023).
Two-Way Score incorporates the similarity be- tween the edits in the Top-K data and the input data. The final score is:
We use T-Patcher and ROME as the baseline edi- tors combine with our framework, denoting them as RASE-Patcher (R-Patcher) and RASE-ROME (R-ROME) respectively. We edit on Bert-base (De- vlin et al., 2019), Bart-base (Lewis et al., 2020) provided by Meng et al. (2022), and GPT-2 XL! models. For R-Patcher, we insert five neurons to the last FFN layer on the FEVER and ZsRE datasets. We training the fact and sentence Encoder with a minibatch consists of 64 facts. For each fact, we select two consistent sentences as positive exam- ples, other facts and sentences related other facts
score; score Cos(H, H:), k
(5)
where H, means the edit embedding calculated by (r) for the edits corresponding to the Top-K fact. Finally, we re-select the Top-K scores from score, and use Eq.4 to identify the edits.
4 Experiments
4.1 Dataset and evaluation metrics
We evaluate RASE on fact-checking dataset FEVER (Thorne et al., 2018a) and Zero-Shot Rela- tion Extraction (ZsRE) dataset (Levy et al., 2017).
hhtps//hhugingfiacccokgg222

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serve as negative examples. The threshold in Eq.4 is f 0.9, ta- 0.15 and t, = 0.05. The Top-K used in Two-Way score 5. Other parameters, like learning rate, will be set as those in the ROME and T-Patcher. All of our experiments were imple- mented on single NVIDIA A100 GPU. See more details in Appendix E.
maintains consistent performance while demon- strating an advantage in generalization. We also test RASE on ZsRE with 4500 edit conditions, fur- ther validating its stability and sustainability. In contrast, T-Patcher's 8 efficiency decreases as more modifications are made due to the continual addi- tion of parameters to the language model. It be- comes challenging for T-Patcher to perform large- scale consecutive edits within certain time. For SERA, as the number of editing increases, its ac- curacy in discriminating modified data decreases, resulting in poor performance on TrainR and TestR. And MEND is a network editing method that predicts parameter changes for modifying current data by learning gradients of editing data. The predictions of MEND are highly dependent on the parameters of LMs, while continuous editing leads to constant parameter changes in LMs, rendering MEND ineffective for achieving sequential mod- ifications. On the other hand, RASE maintains consistent efficiency even as the number of edit data increases.
4.3
Experimental Results
4.3.1 Editing Small scale Models
Table 1 and Table 2 present the editing results on small scale models with R-Patcher. We first evalu- ate RASE on small number of edits.
Table 1: Results on small number edits. R-Patcher is RASE-Patcher, +Pt is the results after use the fact information, +Eq denotes that we add the equivalent inputs during editing based on R-Patcher+Pt.
ZsRE (N=140)
Editor
SR
ER
GR
TrainR
TestR
FT(last)
1.000
0.300
0,580
0.914
0.924
FT
1,000
0.280
0.570
0.923
0.933
MEND
0.410
0.000
0.370
0.000
0.000
SERA
1,000
0,980
0,900
0.906
0.901
Table 2: Results on large number of edits. N denotes the number of edits we edit, +ChatGPT means we use model (+Pt) ), means we use the DDr as the edited dataset, so we did not calculate the retain score on Drr--
T-Patcher
1.000
0.990
0.820
0.997
0.996
the ChatGPT to enhance
R-Patcher
1.000
0.978
0,877
0.953
0.973
+Pt 1.000
1.000
0.976
0.976
0.964
+Eq
1.000
1.000
0.992
0.977
0.968
FEVER (N=60)
Editor
ZaRE
Editor
SR
ER
GR
TrainR
TestR
SR
ER
GR
Traink
TestR
N
FT(last)
1.000
0.590
0.610
0.893
0.946
T-Patcher
0.99
0.97
0.81
0.912
0.948
2766
FT
1,000
0.450
0.530
0.968
0.998
FT
1.00
0.14
0.69
0.936
0.974
2766
MEND
0.040
0.060
0.030
0.349
0.652
SERA
1.00
0.90
0.728
0.694
2766
SERA
1.000
1.000
0.890
0.904
0.916
R-Patcher
1.00
0.95
0.863
0.973
0.975
2766
T-Patcher
1.000
1.000
0.820
0.999
1.000
+Pt
1.00
0.95
0.93
0.976
0.964
2766
R-Patcher
1.000
1.000
0,902
0.961
0.990
+Bg
1.00
0.96
0.97
0.976
0.965
2766
+Pt 1.000
1.000
1.000
0.961
0.986
+ChatGPT
1.00
0.95
0.93
0.999
0,995
2766
+Eq
1.000
1.000
1.000
0.945
1.013
R-PatcherPPt
0.94
0.94
0.957
4502
FEVER
Editor
RASE significantly improves editing general- ization. Table illustrates the results for small number of edits. Our method demonstrates compet- itive performance across two datasets and five met- rics. It is noteworthy that the generalization (GR) is improved when guided by factual information (+Pt). We further enhance model generalization by incorporating consistently sampled data(+Eq) into the loss calculation during editing.
SR
ER
GR
TrainR
TestR
N
T-Patcher
1.00
1.00
0.82
0.999
1,000
1231
FT
1.00
0.16
0.54
0.998
1,002
1231
SERA
1.00
1.00
0.89
0.717
0.709
1231
R- Patcher
1.00
1.00
0.92
0.854
0.972
1231
+Pt 1.00
1.00
0.93
0.867
0.973
1231
+Eq
1.00
1.00
0.97
0.867
0.974
1231
+CharGPT
1.00
1.00)
0.97
0.978
0.980
1231
RASE can combine with large-scale language models such ChatGPT to improve perfor- mance. For the FEVER dataset, we achieve good results in modification performance (SR, GR, ER), but there is a decrease in TrainR. This is because numerous sentences are similar to the edits but dif-
RASE can maintain stable performance on more edits. Table 2 illustrates the results for large number of modifications. In comparison to Table 1, other methods experience a decline in performance as the number of edits increases. However, RASE

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fer by only a few words in FEVER. To address this, we employ ChatGPT to re-rank the Top-K results of retriever to evaluate if the input needs modification. We select the input that satisfies one of the three conditions in (4) as the hard data, construct the factual description of Top-K as K-item decision task, and then let ChatGPT solve the problem. If there is no suitable answer, return 'None'. Ap- pendix D shows the usage of ChatGPT. The results with ChatGPT on both FEVER and ZsRE show that combining the results from the large model with our method can further enhancing the accuracy of fact retrieval and identify modified and unmodified data more accurately. thus maintaining the model's performance on the unedited data better.
to MEMIT, Although the overall performance in terms of generalization is relatively low, RASE demonstrates improvements compared to ROME. This further confirms that the fact-enhanced ap- proach can enhance the generalization of different editors.
As the number of edits expanded to 5000, other methods exhibited a significant decline in effective- ness, particularly MEMIT. This is because MEMIT alters many parameters for each modification. As the edits accumulate, the original parameters of the model become disrupted, leading to the complete failure of the model. In contrast, RASE maintains favourable results and revalidating our approach's 's stability and sustainability.
4.3.2 Editing on large LMs
4.4 Analysis
We use the GPT-2 XL (1.5B) to test the perfor- mance on a large-scale model.
4.4.1
Retrieval analysis
Retrieval architecture analyse. We compare dif- ferent encoder architectures which are treated as retriever. The results are shown in Table 4. When directly using the Bart model as the encoder, the model struggles to identify which data needs modi- fication. Izacard al. (2022a) has achieved good results, but has a lower HIT metric, indicating that it can successfully retrieve similar data but struggles to accurately identify some similar but different samples. After using RoBERTa with con- trastive loss we proposed, there was a significant performance improvement. Furthermore, w/o L52F shows that the margin- based similarity further enhance the HIT value and improving the overall recognition rate.
Table 3: Editing Results on GPT-2 XL. FT-MEM means we include previously modified data and newly mod- ified data as inputs for fine-tuning. R-ROME means we combine RASE with ROME. It is worth noting that in TrainR and TestR, some results exceeding are due the impact of modified data, which unintentionally corrects erroneous data that was not modified.
7sRF
Editor
5R
TrainR
TestR
N
FT
0.515
0.093
0.070
0,261
0.247
1000
PT-MEM
0.454
0.489
0.175
0,356
0.341
1000
MEND
0.006
0.001
0.001
0.009
1000
ROME
0.997
0.520
0,378
0.620
0.572
1000
MEMIT
0.924
0.678
0.503
1.04
1.00
1000
R-ROME
0.997
0.966
0.657
1.002
1.004
1000
ROME++p
0.997
0.972
0.754
1.036
1.007
1000
Table 4: Results on different encoder. E&E and N&N means the model successfully identifies the edits and unmodified data, respectively. EBN represents misiden- tifynng edits as mon-modified data and while NBE repre- sents the opposite. HIT: represents the ratio of success- ful retrieval of the correct key value by the model.
FT
0.447
0.095
0.073
0.461
0.421
5000
ROMB
0.937
0.258
0.173
0,220
0,182
5000
MEMIT
0.433
0.001
0.001
0.001
0.001
5000
R-ROME 0.989
0.946
0.646
1.008
1.001
5000
R- R-ROME+pl
0.997
0.948
0.736
1.133
1.012
5000
RASE can be flexibly combined with other editors and stably edit larger language models. The results are shown in Table 3. When modifying 1000 data in ZsRE, ROME and MEMIT achieve im- pressive results, particularly in TrainR and TestR. However, FT and FT-MEM exhibit lower effec- tiveness, and MEND can not perform any modifica- tions. Moreover, these methods display poor perfor- mance when evaluating the results of previous edits. In contrast, RASE preserves the modified results well without compromising the original model's s performance, Even for TrainR, RASE is very close
Model
E&ET
EBN]
NBEL
N&NT
HIT
Base
0.046
0.953
0.012
0.987
0.39
Contriever
0.914
0.085
0.071
0.928
0.936
La
0.965
0.034
0.048
0.951
0.991
- wlo Leus
0.955
0.044
0.037
0.962
0.989
w/o LsaF
0.745
0.254
0.013
0.986
0.958
w/o Legs + Lsur
0.615
0.384
0.079
0.920
0.971
Relation for Retrieval augmented and Editing. Knowledge editing (KE) is an 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

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on those revised instances while keeping other irrel- evant knowledge unchanged. Retrieval augmented models design retrieval strategies to incorporate world knowledge into a fixed LLM through prompt- ing. Both of them are re-training free and have been shown to be effective for the issue of knowledge staleness.
model editing. For R-Patcher, we save extra neu- rons for each edit, while for R-ROME, we save weight offsets for each edit. In T-Patcher, a portion of the training data is sampled to preserve locality to calculate memory loss. We estimate that storing 40,000 data would require approximately 120MB of space, and additional costs would be associated with maintaining the memory. Similarly, ROME also requires around 160MB of additional storage to maintain locality. contrast, we use memory to save the previous edits. The cost for 1000 edits with R-ROME is 7SMB, and 300MB for R-Patcher. Furthermore, the increase in memory usage is a fixed cost that does not increase with the number of edits.
However, Model editing methods prefer to edits LLM parameters directly, to update LM's knowl- edge, hard to maintain the other knowledge which are correct. Meanwhile, retrieval augmented models may not guarantee LMs can always update their predictions when given retrieved knowledge, due to the LLMs may prioritize their own para- metric knowledge and ignores the retrieved non- parametric knowledge which be called knowledge conflict.
Regarding the Editing layer, we follow the same settings as T-Patcher and ROME regarding the editing layer. For T-Patcher, we edit the FFN Forwa-d Network) in the last layer of the Transformer. For ROME, in GPT2-XL, we edit the FEN layer in the 17th layer. However, as Hase et al. (2023) suggests that "Many layers could store fact, and it happens that some do." Therefore, there may be better choices for continuous editing than continuously modifying a specific layer. It would be beneficial explore more flexible editing strategies by incorporating interpretability the future.
In this paper, we combine the advantage of re- trieval augmented and editing methods. The re- trieval module identifies the edits, and the editing module corrects the erroneous data. In our setting, the editing module processes only one data point at a time, so the retrieval module has greater im- pact on ER, TrainR, and TestR, while the editing module has greater influence on SR and GR.
4.4.2 Cost analysis
Regarding editing efficiency, note that the ef- ficiency of the existing continuous editing method, T-Patcher, gradually decreases as the number of ed- its increases. Our tests found that using T-Patcher to modify 1000 edits on an A100 GPU takes 2-3 days. On the other hand, RASE can perform edits at a stable speed. It takes approximately 50 seconds to modify data. On GPT-2 XL, our efficiency re- mains consistent with ROME and MEMIT, but our method enables more sustained and reliable modifi- cations. The time required to continuously modify 1000 edits shown in Table
4.4.3 Case study
Figure gives a sense of how RASE would perform in practice, our approach can accurately modify X1-X3 without being affected by the content in memory K4, thus keeping X4 unchanged. On the hand, while T-Patcher can also correct X1-X3, it makes mistake X4, due the influence of X3. This is why we adopt retrieval enhancement instead of directly modifying the model parameters.
5 Related Works
Table 5: Time cost for different Editor on 1000 edits.
5.1 Knowledge Editing
Methods
Time
Editing parametric knowledge is not as straight forward (Pan et al., 2023) as editing (Wang et al., 2009, 2010, 2014) knowledge graphs (Pan et al., 2017a,b). For editing parametric knowledge, a natural way is to use constrained fine-tuning update parameters based on new examples (Zhu et al., 2020). However, in PLMs, minor param- eter change could change the model's predictions on many samples. Then, De Cao et al. (2021); Mitchell et al. (2022a); Han et al. (2023) trained a
FT
days
ROME
< 1 days
MEMIT
< I days
T-Patcher Ours
days
I days
Regarding the Extra memory cost, our re- trieval augmented framework uses additional mem- ory to store previous edits to achieve sequential

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accessing memory units and an external look-up ta- ble. Borgeaud et al. (2022); Lazaridou et al. (2022); Izacard et al. (2022b) showed that retrieval im- proves performance across a variety of tasks such as question answering (K (Kwiatkowski et al., 2019), fact checking (Thorne et al., 2018b), dialogue (Di- nan et al., 2019). Mitchell et al. (2022b) proposed memory-based approach for knowledge editing. Inspired by retrieval, we view the editing task retrieval and augmentation process, construct a memory to store editing data, apply certain modi- fication through retrieval, and achieving stable and continuous editing. It is worth noting that, the edit- ing is motivated by a list of bad cases of the form (question, wrong-answes correct- correct-answer), where the correct-answer is the knowledge that we men- tioned above. Therefore, we only retrieve from all the facts related to the data that needs to be mod- ified. And we assume that we have known all the erroneous and their corresponding correct answers.
Lingratic
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Figure 4: Example of Editing Steps.
Hyper-network to predict the parameter shift. Other methods like Meng et al. (2022, 2023) proposed direct editing method and achieved great results on batch editing. More recently, some methods have developed external modules for edits and do not re- quire access to base model parameters (Dong et al., 2022; Mitchell et al., 2022b). In order to apply the editing to real-world challenges better, Huang et al. (2023) proposed the Sequential Model Editing task and trained Transformer-Patchee achieving edit the model up thousand times continuously.
6 CONCLUSION
This paper focuses on sequential model editing and proposes RASE, a retrieval augmented sequential model editing framework, to enhance the perfor- mance of existing editors in plug-and-play man- ner, and achieve efficient, stable, and expansible Sequence Model Editing. We construct fact-patch memory in a self-supervised way and utilize the memory to enhance the model's continuous edit- ing capability. During editing, we use fact infor- mation related to the modified data as prompt to enhance the generalization of the editor. RASE has achieved favourable results under different scales of language models and varying numbers of edits. Additionally, it can be flexibly applied to different editors and integrating with large language models like ChatGPT can further enhance editing perfor- mance.
5.2 Contrastive Learning
The key idea of contrastive learning (CL) is to pull semantically similar samples close and keep differ- ent samples apart (Hadsell et al., 2006; Chen et al., 2020). By employing contrastive learning objec- tives. Gao al. (2021); Yan al. (2021); Chuang et al. (2022); Zhou et al. (2022) fine-tuned the pre- trained language models, resulting in significant advancements in learning unsupervised sentence embeddings. In order to alleviate the need for an an- notated dataset, Gao et al. (2021); Liu et al. (2021) proposed a simple contrastive learning framework that used dropout noise within transformer layers generate positive pairs. Nishikawa et al. (2022) proposed a contrastive learning method for learn- ing sentence embeddings between sentences and their related entities sampled from Wikidata.
In the future, on the one hand, we plan to inves- tigate knowledge editing for some complex tasks, such as reasoning. And explore how to integrate retrieval methods with model editing better. On the other hand, we might look into the connections between editing parametric knowledge and knowl- edge editing for uncertain knowledge graphs (Pan et al., 2005; Stoilos et al., 2006; Pan et al., 2007; Qi et al., 2007; Şensoy et al., 2013).
5.3 Retrieval-augmmented language model
Retrieval augmentation can enhance language mod- els' performance without significantly increasing the parameters and computation (Tirumala al., 2022; Mialon et al., 2023). Khandelwal et al.
(2020) increased PLMs memorization capacity by

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Acknowledgements
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4207-4218.
This work has been supported by the Sci- ence and Technology Cooperation and Ex- change Special Project of ShanXi Province (No. 00.22220404100000100 by the National Natural Science Foundation of China (No.62076155), by the Key Research and Development Program of and by
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.
Shanxi Province (No. 202 2022202220001111 the Chang Jiang Scholars Program (I2019032).
Jacob Devlin, Ming-Wer Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understand- ing. In Proceedings of the 2019 Conference of the North American Chapter the Association for Com- putational Linguistics: Human Language Technolo- gies, Volume 1 (Long and Short Papers), pages 4171- 4186.
Limitations
Our method focuses on editing factual knowledge, which is relatively easy to formalize and evaluate. Future work need to develop universal approaches that can edit all kinds of knowledge such as lan- guage and common sense knowledge in the same way. Additionally, the current metric for judging successful modifications is limited whether the current input has been corrected. However, deter- mining whether the model understands the under- lying facts remains challenging and requires more rigorous evaluation metrics.
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019. Wizard of wikipedia: Knowledge-powent agents. In International Conference on Learning Representations.
conversational
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei 2022. Calibrating factual knowledge in pretrained language models. In Find- ings of the Association for Computational Linguistics: EMNLP 2022, pages 5937-596999 0597-597, Abu Dhabi, United Arab Emirates. Association for Computational Lin- guistics.
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A Contrastive Learning for Embedding
higher than a certain threshold:
To learn sentence and fact representation, we train- ing two Encoders Eney(-) and E-c,(-) to get the fact embedding E and sentence embedding Fi respectively. We construct a minibatch con- taining facts, for each fact Ji. there are S corresponding positive sentence 8,², E [1, L). j E [1, S] , and any sentence unrelated to fi and any fact other than fi serve negative ex- amples for J.. The input batch is denoted as X 81,, ss....,, .....; 81 ....., fr. sL ,L8 "s...... $1 We train (-ce(-) and (.)c using three contrastive losses and use RoBERTa (Liu et al., 2019) as the baseline PLMs. For convenience, we use and s denote the Embedding E = (f) and Fg = Encc($) respectively:
Loss$2F -log c Ps(s)
(8)
where E [1, m, n is the margin value for posi- tive and negative pairs. Ps(.) and Ns(.) are denoted as the positive and negative similarity scores re- spectively.
Ps fi, - (cos( m)/r
(9)
For negative pairs, we will them a punishment when = cos(s;, ( fi) cos(s), fÿ) less than m.
(cos(s), fz) + n)/T, h < m, (oos(8;:
(s.s;
else. (10)
Facts Contrastive Learning (F2F) aims to learn fact embedding in unsupervised way. Given two embedding with different dropout masks and ft. the training loss is:
In summary, our total loss for Contrastive Learn- ing is:
goim(tnSiii D/T
Loss = AoLoss + LoLosss2s + AgLoss
LossF2F = -log
(6)
(11)
=== (sim(fuss)t??
where A; is
We train the encoder separately on ZsRE and FEVER datasets. It is worth noting that the dataset used for training the encoder and the dataset used for editing are sampled separately. And these en- coders are pre-trained before the editing process. During editing, we utilize these encoders only. The L is 64, and the maximum value of S is 5.
where T is a temperature
and
sim(.) is the cosine similarity.
Sentences Contrastive Learning (S2S) aims to learn sentence embedding in unsupervised way. Since there are S sentences in the same batch which are both related to the same fi, we treat S sentences as a group and learn sentence representations by the loss:
B Detail of the base editor
Transformer-Patcher In this paper. we follow (Huang et al., 2023), use Transformer-Patchtee as a based editor because it only requires training a certain number of neurons for each edit and insert- ing them at designated layer of the Transformer. In contrast to Huang al. (2023), the patch used in our paper does not require additional memory to store training data to satisfy locality. Moreover, to enhance the generalization of the editor, we pro- pose a fact-guided editor. We concatenate the fac- tual information obtained through retrieval with the input data as prompt to improve the generaliza- tion of the editing process.
Loss llog- E/ ()mm(=)-))/=+*
where 9g = (s! 8?, .... sg is the mean repre- sentation of group of inputs related to the same factual ft. (ool(.) is the mean-pooling function, and s is the dropout represents about 8g.
Contrastive Learning between Sentence and Fact (S2F) pulls the embedding of inputs and their related fact close while keeping unrelated facts apart. We use a margin-based similarity to in- crease the similarity between negative samples and weaken the similarity between positive samples, to ensuring that the similarity of positive samples is
For an edit (Fes Mnee) and the fact describe Fr.. the input for the edit is X [Ie: Fz..). and then we add few neurons to the FFN of special Transformer layers to alter the output of the edit. Formally, denote the hidden state of X, is h, for a standard
As shown in Figure 3, the fact is "Iphone 14 is developed by Apple Inc.", and the postive sentence is "Iphone 14 was created hy?", the negative sentence is "What club does Mossi play for?".

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FEN in Transformer blocks, the output of (h) is calculated by:
C Memory maintenance and usage
To further enhance the efficiency of memory re- trieval, we employ the following methods to main- tain the memory.
Act(hh K + by),
(12)
(h) a V bus
(13)
1) Adding: When new edit has never appeared in the memory, we store the corresponding key- value pair. The key is the fact embedding about the edit and Encoder by the fact encoder Eney(-), and the value the editing operation and the fact description for the edit.
where A-t(-) is non-linear activation function such as Relu, K E 1ddidd2 and V E IRd2ad1 are the weight matrix of two linear in FFN respectively, by and by are two bias vectors. After add extra neurons (Kn E Idien Vn R in the FFN, the new output of FFN,, (h) is calculated by:
2) Updating: When the key of a new edit is similar or identical to an existing key in the memory M, we merge the corresponding modification data with the existing one in the memory and train them together.
[a an) = Act(h. [K kn] + [bk bxl),
(14)
V
(h) = [a an)
bu-
(15)
Vn
Substituting the Eq. (13) into Eq. (15) and calcu- lating the following:
After the facts-patch memory constructed, we will use the query module (section 3.2) to judge if each input needs to be edited, If not, the input will calculate by the original LMs. Once the in- put is the edit, we will apply the corresponding value to LMs temporarily. For T-Patch, we insert the trained extra neurons to the last layer's FFN of Transformer; for ROME, we use the k, and e, to calculate the parameter shift AW and replace the LMs parameters W of layer 7 by W W + AW. Once the current edit computation is completed, the model will be reset. Therefore, when we can successfully identify which data requires modifica- tion, our method ensures that the performance of the model on unmodified data remains unaffected.
FFN,, (h) = (h) + On Un-
(16)
After training, we use target label yz. and the edited output uz, to calculate the loss le:
le Ly Uz.).
(17)
where L(.) is a loss function (e.g. The Cross En- tropy Loss).
Rank-One Model Editing (ROME) (Meng et al., 2022) applies a rank one edit to the down- projection matrix in MLP layer in the model. It views the linear operate in Transformer with pa- rameter W as a key-value store for set of vector keys = [A1,, knnk] and corresponding vector values V - [0n, U2, ", by solving W K z V. Each k; and is denote a question and answer, re- spectively. Then for a new key-value pair (ka, v.). we can insert this new data into the target layer by solving an optimization:
D Constructing questions through ChatGPT
For an input s: "Brigitte Macron is married to some- one who is President of the French Republic." we use our score function (c). to select the Top-K facts, the result is:
minimize|| | K - VII.
such that Wk. N+,
(18)
Facts ['Brigitte Macron is the fiancee of Emmanuel Macron.". "Brigitte Macron is en- gaged.". "Brigitte Macron was born on April 23. 1953.", "Peggy Sue Got Married is a 1933 American film."] with the similar score [0.939, 0.917, 0.731, 0.359, 0.354, 0.316].
by selting W = W A(C-
where W is the weight matrix for the original a constant that we pre- cache by estimating the uncentered covariance of k from a sample of Wikidata text, and A = (n= - wh. Wk.)/(C- ) k. is vector proportional to the residual error of the new key-value pair on the original memory matrix. k, is the average value over small set of texts ending with the subject s, and U. is learning by optimization. In this paper, we use the ROME as one of the base editors be- cause it does not need extra training and editing each data efficiently.
linear, C = KKT
However, the fact "Brigitte Macron is the fi- ancee of Emmanuel Macron." " is not equivalent to "Brigitte Macron is married to somcone who is President of the French Republic.", our method should not edit this input. However, due to the sim- ilar high score, our query function chooses the first fact as the key and edits this data, influencing the result of the language model.

---

For this instance, we constructed multiple- choice question as a prompt:
our method.
Success Rate (SR): For each edit, we test if the post-edit model fi outputs the desired prediction:
Question: Which of the following sentence ex- presses the same meaning as the sentence "Brigitte Macron is married to someone who is President of the French Republic.", If there is no answer, reply "None".
SR Σ t=0
[(f((+)) Vnr)
(19)
Option:
Edit Retain Rate (ER): After edited 7 edits, we evaluate how many past edits retained by the final model fT:
A): Brigitte Macron the fiancee of Emmanuel Macron.
B): Brigitte Macron is engaged.
C): Brigitte Macron was born on April 23, 1953.
ER 1
T E [ff((a)) ye.).
(20)
D): Peggy Sue Got Married is a 1933 American film.
Then, utilize ChatGPT to answer the question and combine the answer as retrieval results with framework, enabling better assessment of whether the data needs modification.
Generalization Rate (GR): After edited T edits, we evaluate if fr is edited success on the equivalent dataset of the edit example in Dedit:
T N
E Details of Experiments setting
GR = TN 0=
I(fr(a)) = Voj), (21)
We evaluate our models on fact-checking dataset FEVER (Thorne et al., 201 8a) and Zero-Shot Rela- tion Extraction (ZsRE) dataset (Levy al., 2017). We apply the BERT-base model Devlin et (2019)
where N₂ is the number of the i-th edit equivalent input.
Training Retain Rate (TrainR): After edited T edits, we compare the performance of the final model of fr and the initial model fo on subdataset Dr which is randomly sampled from ID Breain
the FEVER dataset. For ZsRE, we apply the BART-base model Lewis al. (2020). We use the same data splits for both datasets as Huang et al. (2023). We use the original validate and employ it as Drest and the original Drrain is split into an edited set Medit a new training set ID) Brain and new validation Deal in ratio (0.8:0.8:0.1 for FEVER and 0.9:0.075:0.02. for ZsRE).
TrainR I(anjED. I(f((z) = y) E(an) T(fo(z) = =)
(22)
Test Retain Rate (TestR): After edited 7 edits, we compare the performance of the final model of fr and the initial model fo on original validate dataset Drest.
We denote the initial model as fo which is trained on D Dirain and validated on Dvat- Then the model fo is sequentially edited on Dodit- For FEVER, the accuracy of the initial model is 87.6% on the edited dataset, 94.6% on the train dataset, and we get 10496 instances for the edited dataset; the mistake data is about 1300. For ZsRE, the accuracy of the initial model is 47.1% on the edited dataset and 56.9% on the training dataset; as a result, we get 5352 instances for the edited dataset, and the mistake data is about 2800. For both datasets, we randomly sampled subset from D'rrinn with the size of 10,000 as Btr, and we training the fact Encoder and sentence Encoder on D' D'rrin without DD, and validate on Deat-
TrainR = Elase I(f((z) = =))
(23)
Elayet 1(fo(z) = v)
Our baselines include:
Fine-Tuming (FT) (Zhu al., 2020): Directly fine-tunes the model on the edit example.
FT with KL divergence (FT+KL) (Zhu et al.. 2020): Fine-tunes the model on the edit ex- ample with an extra Kullback-Leibler (KL. constrained.
And suppose there T mistakes in Dedit represents the indicator function, after editing the t-th edit example (xt, Vn,), we obtain a edst-cdit model fr, and we use the following rate to evaluate
MEND (Mitchell at.,, 2022a): Using hyper- network to learn a parameter shift and then apply it to the model.

---

SERA (Mitchell et al., 2022b): A variant of memory-based model editor, which is pro- vided by Huang et (2023).
F Dataset Samples
Figure shows an example in ZsRE. For the triplet information involved in the question "<Cari Leke- busch, date_ož birth, 1972>". we convert it into similar question format "Cari Lekebusch When did Cari Lekebusch get born? II 1972" and use it as factual description. When using facts to hance generalization, we remove the labels: "Cari Lekebusch When did Cari Lekebusch get born?". Figure 6 shows an example FEVER. The struc- ture for FEVER is similar to ZsRE, but the differ- ence is that we construct faac_ree__att repuuse based on the statements and actual facts from FEVER. e.g. For the fact: <Amerigo Vespucci.place_o/ biitth Italian>,
ROME (Meng et al., 2022): A Locate and Edit method for decoder-only models.
MEMIT (Meng et al., 2023): method exten- sion of ROME that modifies the MLP weights of a range of critical layers.
T-Patcher (Huang al., 2023): sequential editing method which adds and trains a few neurons in models.
the fact represent: "Amerigo Vespucci Il Amerigo Vespueci was Spanish. II False", due the claim "Amerigo Vespucci was Spanish.". "
pst
Noatte the Jate of sieth of cae talere coae Nost tert uduteschhs is satte biless
2:
Text 1gg
Lebobusth, cate.st Mirth, 2072"
vell lests
Inpal:
"Seerigh Mrspare ses Sparid
Figure 5: Case 7sRE.
Maveriao Vossani wo Sparish Meniss
"heerig Wagaeca wa "parosk."
"euerigo Vespace staocurt burth, Laalies" Masnigg Wospard"
Figure 6: Case in FEVER.


Reviews:
[{"review_id": "CrKLIHclBS", "reviewer": "Reviewer_z2e8", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "Thank you very much for your suggestions, and we also appreciate your approval of our work!\n\nIntegrating the knowledge graph with model editing is interesting. In the future, we hope to utilize the knowledge graph to achieve propagative edits and to extend model editing to richer types of 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 paper proposes a general framework/solution for the large sequential model editing. \nExperiments have shown the method can be easily applied to other existing approaches. \nIn addition, the experiment results show the proposed model is so effective that the improvements are quite significant. \nThe paper is very well written with clear conclusion and methodology. \n", "reasons_to_reject": "It would be better to consider various factual information like the information from knowledge graph. This can be a future work. \n", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper is trying to improve the sequential model editing by retrieval based framework. \nIt defines a framework leveraging the self-supervised trained fact and sentence encoder to maximize the similarity among sentences and relevant fact description. Then, the Fact is used as key while the parameter shift is regarded as value. Given one input, it identifies a key and applies the parameter shift. \nExperiments are conducted on fact checking dataset and relation extraction dataset. Experiment results can clearly demonstrate the strength of the approach. ", "excitement": 5, "reproducibility": 5, "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": "xbwKHUF69C", "reviewer": "Reviewer_Eqru", "summary": "", "questions": "1. Why does the MEND have a poor performance in all experiments, especially on the ZsRE dataset?\n\n2. Why don’t you use the ChatGPT to enhance your model on the ZsRE dataset?", "limitations": "", "rating": 3, "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": "Thank you for your suggestions and comments!\n\n**Q1. The paper lacks some essential experiments. For example, more details of the editing speed and extra memory should be provided if the proposed method is different from previous methods on editing speed and extra memory.**\n\nWhile the main focus of our paper is to enhance the upper limit and stability of sequential model editing, we do provide a brief analysis in Section 4.3.1 paragraph 3 and Section 4.4 paragraph 2 on memory cost and editing efficiency. For your convenience, below are some further analysis, which we will supplement in the appendix of the camera ready version.  \n\n**Regarding the Extra memory cost**, as discussed in section 4.4, to modify 3000 records, R-Patcher requires 300MB of storage space while T-Patcher needs 420MB, as T-Patcher needs 120MB for initialisation, which is not needed in R-Patcher. ROME needs only 160MB memory for editing, as it does not store the previous edits but rather update the parameters of the model directly.\n\n**Regarding editing efficiency**, in section 4.3.1, we note that the efficiency of the existing continuous editing method, T-Patcher, gradually decreases as the number of edits increases. Our tests found that using T-Patcher to modify 1000 edits on an A100 GPU takes 2-3 days. On the other hand, our method can perform edits at a stable speed. It takes approximately 50 seconds to modify a data. On GPT-2 XL, our efficiency remains consistent with ROME and MEMIT, but our method enables more sustained and reliable modifications. The time required to continuously modify 1000 edits is:\n\n| Methods | Time |\n| --- | --- |\n| T-Patcher | 3 days |\n| FT | 2 days |\n| ROME | < 1 days |\n| MEMIT | < 1 days |\n| Ours | < 1 days |\n\n**Q2. Although the proposed method is interesting, some parts of this framework are not so novel, such as contrastive learning methods in the self-supervised encoder training module and memory construction.**\n\nIn contrast to the previous SME approach, we are the first to adopt a retrieval-augmented strategy to achieve continuous model editing. Through retrieval augmentation, we can:\n\n- Efficiently make sequential modifications to the model's knowledge while quickly adapting to models of various scales;\n- Avoid compromising the model's original performance and ensure controlled editing or modifications, which is crucial in applications.\n\nAdditionally, the techniques we employ have been enhanced based on the editing tasks:\n\n- The retrieval under editing tasks requires a high demand for Top1(R@1) accuracy and the ability to identify semantically consistent data. Therefore, we incorporate margin-based similarity and group loss into the contrastive learning loss separately to enhance the performance of the retriever.\n- Existing memory-based methods give less consideration to the cost of memory maintenance. At the same time, we take the representation of modified data as the key and the change of modified parameters as a value. In this way, our method has the following advantages:\n    - Achieving editing in a plug-and-play manner.\n    - For frequently changing facts, we can still quickly locate and update this information, thus achieving cost-effective maintenance.\n\n**Q3. The experiment results show that the proposed method has poor improvement. Actually, the proposed method gains a worse performance on some metrics.**\n\nOur work primarily addresses the issue of the sustainability of Sequential model editing. As shown in Sec.4 Table 1 and Table 2, as the number of edits increases, our method continues to maintain stable performance and outperforms existing methods. Furthermore, our approach achieves breakthroughs in sustained editing numbers, which is one of the main objectives of this paper.\n\nThe slightly “worse performance on some metrics” is mainly concentrated in a small number of edits, while our advantage lies in conditions involving substantial edits, as shown in Sec.4 Tables 2 and Table 3. As the amount of data being modified increases, the superiority of our method becomes increasingly apparent, and our overall metrics outperform those of existing methods.\n\nFurthermore, existing editing methods alter the model parameters and the inherent lack of interpretability of the model, the effects of even minor parameter perturbations are difficult to estimate. As a result, both TrainR and TestR are unreliable. In contrast, we achieve edits while keeping the model parameters unchanged, resulting in more reliable TrainR and TestR values.\n\n**Q4. The paper is hard to read, it could perhaps be alleviated by providing guided examples that help to understand.**\n\nSure, in the following example, our approach can accurately modify X1-X3 without being affected by the content in memory K4, thus keeping X4 unchanged. On the hand, while T-Patcher can also correct X1-X3, it makes a mistake on X4, due to the influence of X3. This is why we adopt retrieval enhancement instead of directly modifying the model parameters.\n\n**Examples:**\nAssuming we have an input sequence:\n\n*X1: Which team does Messi play for?*\n\n*X2: In which team does Messi play?*\n\n*X3: Which team does Kylian Mbappé play for?*\n\n*X4: What country does Kylian Mbappé play in?*\n\nThe original language model's output to X1, X2,X3 and X4 are as follows:\n\n*LMs(X1) = A1: Paris Saint-Germain. (out of data)*\n\n*LMs(X2) = A2: Paris Saint-Germain. (out of data)*\n\n*LMs(X3) = A3: Edmonton Oilers. (Wrong)*\n\n*LMs(X4) = A3: French. (Right)*\n\nEditing Steps:\nStep 1: Pre-Constructed memory containing 4 records (Key-Value Pairs),suppose we have modified the following 4 pieces of data consecutively (*Enc(x) denotes the representation after encoding x using the Encoder Enc.):\n\n*K1: Enc(Messi win the 2022 FIFA World Cup.) -- V1*\n\n*K2: Enc(Messi Played for Inter Miami.) -- V2*\n\n*K3: Enc(Mbappé played for Paris Saint-Germain) -- V3*\n\n*K4: Enc(Messi is from Argentina.) -- V4*\n\nStep 2: Retrieve whether there is information related to input in the memory.\nRetrieval Results:\n\n*X1:K2,V2*\n\n*X2:K2,V2*\n\n*X3:K3,V3*\n\n*X4:None,None*\n\nStep 3: Apply each retrieval result to LMs and Inference.\nOur method: the post-edit model output sequence is:\n\n*LMs(X1,V2) = A1:Inter Miami. (Edit Success!)*\n\n*LMs(X2,V2) = A2:Inter Miami.  (Edit Success!)*\n\n*LMs(X3,V3) =A3:Paris Saint-Germain.  (Edit Success!)*\n\n*LMs(X4,None)=A4:French  (Remain)*\n\nBut the T-Patcher post-edit model results are as follows:\n\n*LMs(X1) = A1:Inter Miami.  (Edit Success!)*\n\n*LMs(X2) = A2:Inter Miami. (Edit Success!)*\n\n*LMs(X3) =A3:Paris Saint-Germain. (Edit Success!)*\n\n*LMs(X4)=A4: Paris (affects!)*\n\n**Q5. Why does the MEND have a poor performance in all experiments, especially on the ZsRE dataset?**\n\nMEND is a hyper-network editing method that predicts parameter changes for modifying current data by learning gradients of editing data. The predictions of MEND are highly dependent on the parameters of LMs, while continuous editing leads to constant parameter changes in LMs, rendering MEND ineffective for achieving sequential modifications.\n\n**Q6. Why don’t you use the ChatGPT to enhance your model on the ZsRE dataset?**\n\nWe are using ChatGPT to leverage its semantic computing capability to assist in the retrieval process, thereby enhancing the quality of search results. We didn't report the use of ChatGPT to enhance the model on the ZsRE dataset because our method already large outperforms T-Patcher on the ZsRE dataset. To address your concerns, we report the results of ChatGPT on ZsRE as follow and will include them in the camera ready version. \n\n| N=2766 | SR | ER | GR | TrainR | TestR |\n| --- | --- | --- | --- | --- | --- |\n| T-Patcher | 0.99 | **0.97** | 0.81 | 0.912 | 0.948 |\n| R-Pathcer | 1.00 | 0.95 | 0.863 | 0.973 | 0.975 |\n| +Pt | 1.00 | 0.95 | 0.93 | 0.976 | 0.964 |\n| +ChatGPT | **1.00** | 0.95 | **0.93** | **0.999** | **0.995** |", "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": "1. This paper focuses on timely and sustainable correction and unknown to-be-modified data, which is different from previous work.\n\n2. The proposed framework can be flexibly combined with other editors, showing its expandability.", "reasons_to_reject": "1. The paper lacks some essential experiments. For example, more details of the editing speed and extra memory should be provided if the proposed method is different from previous methods on editing speed and extra memory.\n\n2. Although the proposed method is interesting, some parts of this framework are not so novel, such as contrastive learning methods in the self-supervised encoder training module and memory construction.\n\n3. The experiment results show that the proposed method has poor improvement. Actually, the proposed method gains a worse performance on some metrics.  \n\n4. The paper is hard to read, it could perhaps be alleviated by providing guided examples that help to understand.", "questions_to_authors": "1. Why does the MEND have a poor performance in all experiments, especially on the ZsRE dataset?\n\n2. Why don’t you use the ChatGPT to enhance your model on the ZsRE dataset?", "paper_topic_and_main_contributions": "This paper presents a retrieval-augmented editing framework to enhance the performance of existing editors in a plug-and-play manner. The authors first train a fact encoder and a sentence encoder in a self-supervised way. They construct a fact-patch memory and use a query module to classify the edits by retrieving related facts from the memory. The experiments validate the efficiency and generalization of this framework.", "excitement": 3, "reproducibility": 4, "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": "4zp9rtlYHi", "reviewer": "Reviewer_Gyuf", "summary": "", "questions": "\n- Could you provide any analysis on more real-world PLM mistakes to give a better sense of how the approach would perform in practice?\n- Are there any further ablation studies you could do to better understand the source of improvements - is it the retrieval primarily or the fact representations?\n- How does performance scale beyond the thousands of edits tested? Where do you see the limits? \n- Is related factual knowledge always needed for good performance? How critical is access to this information?", "limitations": "", "rating": 3, "confidence": 3, "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": "Thanks for your suggestions and comments!\n\n**Q1. The datasets used are somewhat artificial. More analysis on real-world mistakes could better highlight the benefits.**\n\nThe datasets chosen (ZsRE and FEVER) are commonly used for editing tasks, with FEVER based on Wikipedia pages and reflects concepts in the real world. Maybe the example presented below in Q6 could help highlight some of the benefits.\n\n**Q2. There could be more ablation or analysis to understand exactly where the gains come from - is it mainly the retrieval or the fact representations?**\n\nOur editing model consists of two core modules: the retriever and the editor. They are responsible for different metrics. In general, the retriever has a greater impact on the metrics, as it determines whether the current input needs to be modified. If modification is necessary, the corresponding changes are applied to the model. Consequently, when data that doesn't require modification is mistakenly identified as needing modification, the TrainR and TestR are lowered. Similarly, when data that needs modification is wrongly classified as not needing modifications, the ER and GR are decreased. On the other hand, the SR metric indicates the success of the editor in modifying data that requires changes. Therefore, the SR is solely related to the performance of the editor.\n\n**Q3. The efficiency gains flatten out after thousands of edits, suggesting there may be limits to scaling further. More discussion of limitations would be helpful.**\n\nOne of our contributions is to increase the upper limit of sequential editing while maintaining performance. Experimental results have also shown that our approach yields favorable results when modifying around 5,000 edits. In contrast, other methods exhibit poor performance, low efficiency, or entail significant editing costs.\nRegarding the \"limits to scaling\", we believe they still exist. The primary reason is that with the growing number of edits, instances with high similarity yet distinct differences could populate the memory. This situation impairs the performance of the retriever, consequently restricting editing upper. There can be different approaches to addressing this but they are clearly outside the scope of the paper. In the camera ready version, we will further clarify this issue in the Limitations section.\n\n**Q4. The approach relies on access to related factual knowledge, which may not always be available in practice. How critical is this knowledge?**\n\nThis knowledge is critical as it is required as inputs according to the definition of  the Sequential Model Editing task (cf. Sec. 2.2). Intuitively, the editing is motivated by a list of bad cases of the form (question, correct-answer), where the corrct-answer is the knowledge that we mentioned above. We believe this task defiinition is rather close to real-world settings. If we do not have such knowledge, then it would be a different task.\n\n**Q5. The gains over prior SME methods, while substantial, are a bit incremental. The novelty may not seem as high.**\n\nIn contrast to the previous SME approach, we are the first to adopt a retrieval-augmented strategy to achieve continuous model editing. Our method allows us to avoid modifying the original model parameters. As a result, while maintaining a certain level of performance, our method exhibits excellent reliability and stability. With the growing prevalence of large models, our method is more promising and better suited to these models.\n\n**Q6. Could you provide any analysis on more real-world PLM mistakes to give a better sense of how the approach would perform in practice?**\n\nSure, in the following example, our approach can accurately modify X1-X3 without being affected by the content in memory K4, thus keeping X4 unchanged. On the hand, while T-Patcher can also correct X1-X3, it makes a mistake on X4, due to the influence of X3. This is why we adopt retrieval enhancement instead of directly modifying the model parameters.\n\n**Examples:**\nAssuming we have an input sequence:\n\n*X1: Which team does Messi play for?*\n\n*X2: In which team does Messi play?*\n\n*X3: Which team does Kylian Mbappé play for?*\n\n*X4: What country does Kylian Mbappé play in?*\n\nThe original language model's output to X1, X2,X3 and X4 are as follows:\n\n*LMs(X1) = A1: Paris Saint-Germain. (out of data)*\n\n*LMs(X2) = A2: Paris Saint-Germain. (out of data)*\n\n*LMs(X3) = A3: Edmonton Oilers. (Wrong)*\n\n*LMs(X4) = A3: French. (Right)*\n\nEditing Steps:\nStep 1: Pre-Constructed memory containing 4 records (Key-Value Pairs), suppose we have modified the following 4 pieces of data consecutively (*Enc(x) denotes the representation after encoding x using the Encoder Enc.\n):\n\n*K1: Enc(Messi win the 2022 FIFA World Cup.) -- V1*\n\n*K2: Enc(Messi Played for Inter Miami.) -- V2*\n\n*K3: Enc(Mbappé played for Paris Saint-Germain) -- V3*\n\n*K4: Enc(Messi is from Argentina.) -- V4*\n\nStep 2: Retrieve whether there is information related to input in the memory.\nRetrieval Results:\n\n*X1:K2,V2*\n\n*X2:K2,V2*\n\n*X3:K3,V3*\n\n*X4:None,None*\n\nStep 3: Apply each retrieval result to LMs and Inference.\nOur method: the post-edit model output sequence is:\n\n*LMs(X1,V2) = A1:Inter Miami. (Edit Success!)*\n\n*LMs(X2,V2) = A2:Inter Miami.  (Edit Success!)*\n\n*LMs(X3,V3) =A3:Paris Saint-Germain.  (Edit Success!)*\n\n*LMs(X4,None)=A4:French  (Remain)*\n\nAs for the T-Patcher post-edit model, its results are as follows:\n\n*LMs(X1) = A1:Inter Miami.  (Edit Success!)*\n\n*LMs(X2) = A2:Inter Miami. (Edit Success!)*\n\n*LMs(X3) =A3:Paris Saint-Germain. (Edit Success!)*\n\n*LMs(X4)=A4: Paris (affects!)*\n\n**The answers to Q7, Q8, and Q9 have been provided in the response to Q2, Q3, and Q4.**", "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": "\n- The paper addresses an important problem of efficiently editing factual mistakes in PLMs, which is very relevant as these models are deployed more widely.\n- The proposed retrieval augmented framework is novel and intuitive. Storing prior edits for later retrieval is a sensible approach.\n- The method clearly outperforms prior SME techniques in terms of efficiency, scalability, and edit retention on the FEVER and ZsRE datasets. The gains are substantial.\n- The ablation studies provide useful insights into the impact of different components like the contrastive learning and integration with ChatGPT.\n- The paper is clearly written and easy to follow overall. The background covers relevant prior work.\n", "reasons_to_reject": "\n- The datasets used are somewhat artificial. More analysis on real-world mistakes could better highlight the benefits.\n- There could be more ablation or analysis to understand exactly where the gains come from - is it mainly the retrieval or the fact representations?\n- The efficiency gains flatten out after thousands of edits, suggesting there may be limits to scaling further. More discussion of limitations would be helpful.\n- The approach relies on access to related factual knowledge, which may not always be available in practice. How critical is this knowledge?\n- The gains over prior SME methods, while substantial, are a bit incremental. The novelty may not seem as high.\n", "questions_to_authors": "\n- Could you provide any analysis on more real-world PLM mistakes to give a better sense of how the approach would perform in practice?\n- Are there any further ablation studies you could do to better understand the source of improvements - is it the retrieval primarily or the fact representations?\n- How does performance scale beyond the thousands of edits tested? Where do you see the limits? \n- Is related factual knowledge always needed for good performance? How critical is access to this information?", "paper_topic_and_main_contributions": "This paper focuses on the task of sequential model editing (SME), which aims to continuously fix a series of mistakes in a pre-trained language model (PLM) as they appear, while maintaining performance on previous edits and unmodified data. The main contributions are:\n- Proposes a retrieval augmented framework that stores edits and related factual information in a \"fact-patch memory\". A query module then retrieves from this memory to identify and apply edits.\n- Introduces a fact-aware contrastive learning method to learn sentence and fact representations for retrieving related facts.\n- Shows the framework can enhance existing model editors by providing relevant facts during editing.\n- Demonstrates stable performance scaling to thousands of edits on FEVER and ZsRE datasets.\n\nThe problem of continuously updating PLMs without damaging overall performance is practically important as these models are deployed in products. The proposed retrieval approach is novel and the results demonstrate clear improvements in efficiency and scalability over prior SME methods.\n", "excitement": 3, "reproducibility": 4, "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": {}}]

Author rebuttal:

Meta-review: This paper studies the task of sequential Model editing (Huang+, 2023) -- fixing erroneous knowledge in LLMs continuously. The speed of existing editors requires continuous modification of LM params and has an increasing cost during sequential editing. 
This work presents a retrieval-augmented editing framework. it borrows the memory-based editing approach (Mitchell+ 2022) and applies it to the continuous setting. It 
1.  trains a fact encoder and a sentence encoder with self-supervised contrastive losses. 
2.  construct a fact-patch memory 
3. uses a query module then retrieves from this memory to identify and apply edits.

Experiments on FEVER and ZsRE datasets shows the framework can enhance existing model editors by providing relevant facts during editing. The approach scales to thousands of edits.

Strength:
1. The paper addresses an important problem of sequential model editing (although the paper has given little context of why this task is important in production settings).  
2. The proposed retrieval approach is novel and the results demonstrate clear improvements in efficiency and scalability over prior SME methods
3. The proposed framework can be flexibly combined with other editors.

Weakness:
1. More details of the editing speed and extra memory should be prominently presented in the paper.
2. I think it would be helpful to discuss the relationship (and pros/cons) comparing SME to retrieval augmented models (e.g. REALM).
3. more ablation study is needed to explain where the gains come from (e.g., the retrieval vs the fact representations).
Decision: Accept-Findings