Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration

Authors: kaihang  pan
zhaoyu  fan
juncheng  li
qifan  yu
hao  fei
siliang  tang
richang  hong
hanwang  zhang
qianru  sun
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-06 19:08:27

Abstract: The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose \textbf{UniKE}, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic levels.  Within such a unified framework, we further promote knowledge collaboration by disentangling the knowledge representations into the semantic and truthfulness spaces. Extensive experiments validate the effectiveness of our method, which ensures that the post-edit MLLM simultaneously maintains excellent reliability, generality, and locality. The code for UniKE is available at https://github.com/beepkh/UniKE.

Main paper: 
 introduction: 
 The rapid development of Large Language Models (LLMs) [35, 36] has made it increasingly important to ensure the real-time accuracy of their outputs in an efficient manner. To this end, in the NLP community, Knowledge Editing [41, 45] has been proposed as a data-and time-efficient way to edit LLMs, correcting errors or outdated responses while ensuring no negative impacts are created. The post-edit model is required to generate the desired output given the input (Reliability), also generalize over other equivalent neighbors of inputs (Generality) without altering the output over other irrelevant inputs (Locality). Knowledge editing methods can be divided into two main categories based on the type of knowledge involved: intrinsic knowledge editing [11, 28] where we update specific model parameters to store new knowledge in a parametric manner; external knowledge resorting [46, 29] that LLMs perceive the new knowledge contained in the relevant context (e.g., via in-context learning). Both types of methods have shown good effectiveness in editing LLMs.Going a step further, with the emergence of advanced multimodal large language models (MLLMs [1] ), there has been a further exploration into Multimodal Editing. Unfortunately, [4] finds that though The generality and locality on MMEdit [4] when applying T-Patcher [11] (intrinsic knowledge editing), IKE [46] (external knowledge resorting), the combination of these two (TP+IKE), and UniKE for multimodal editing. (b) The paradigm of intrinsic knowledge editing (Intrin. KE) and external knowledge resorting (Extern. KE) before and after knowledge unification.efficient in editing LLMs, existing methodologies face considerable challenges for MLLMs due to the inherent diversity and complexity of multimodal knowledge. Despite still maintaining high reliability, they struggle to simultaneously achieve both ideal locality and generality, as shown in Figure 1 .We argue that both approaches, whether intrinsic knowledge editing or external knowledge resorting, have respective drawbacks for multimodal editing. Specifically, intrinsic knowledge editing (e.g., T-Patcher [11] that integrates additional neurons into MLLM) tries to eliminate the risk of losing previously-learned facts and preserve locality. However, it also leads to the newly integrated knowledge resembling rote memorization [3] with weak generality of its truthfulness, as multimodal reasoning requires a coordinated understanding of semantics from multiple modalities. Conversely, though external knowledge resorting (e.g., in-context editing [46] ) retrieves generalizable information from external databases, the in-context knowledge may not have a strong semantic relevance with the original input [23] . This can mislead the MLLM into areas they originally excelled, resulting in weak locality. Figure 2 .a provides direct evidence to support the above discussion.Therefore, how can we effectively edit MLLMs? One intuitive idea lies in directly combining intrinsic knowledge editing with external knowledge resorting, leveraging the advantages of both. However, in intrinsic knowledge editing (such as T-Patcher), the extra integrated knowledge typically incorporates parametric neurons into the model parameters, which is abstract with high-level semantics. Conversely, external knowledge resorting, such as in-context editing, feeds the MLLM with descriptive images and text at the input end, directly describing the content with low-level semantics. Consequently, these two methods exhibit significant differences in paradigms at inconsistent semantic levels and it is challenging to establish a synergistic correlation with each other. Figure 2 .a demonstrates that simply combining T-Patcher and in-context editing leads to both undesirable locality and generality in the post-edit MLLM, highlighting the drawbacks of each approach separately.To address the above issue, we propose UniKE, a novel multimodal editing method that establishes a unified framework for both intrinsic knowledge editing and external knowledge resorting, enabling a synergistic knowledge collaboration. First, we develop a unified view for intrinsic and external knowledge, both represented as vectorized key-value memories at the same semantic levels. Based on this view, we combine both types of knowledge editing methods, executing them in the latent space with a unified paradigm, as shown in Figure 2 .b. Specifically, intrinsic knowledge editing integrates extra knowledge into the internal key-value memory at the feed-forward network; external knowledge resorting leverages an external key-value memory to inject knowledge into self-attention via feature shifting. Both methods could be performed in the same transformer layers with a synergistic correlation, preliminarily allowing each to utilize strengths for complementing the other.Moreover, we further effectively enhance the collaboration between intrinsic knowledge and external knowledge resorting. Within the unified framework, the two editing methods still require emphasis on different aspects of knowledge to further complement their respective drawbacks: intrinsic knowledge should focus on generalizable truthfulness, while external knowledge should have relevant semantics to the input samples. So we leverage contrastive learning to disentangle the knowledge representations into the semantic and truthfulness spaces. In the semantic space, we enable the intrinsic knowledge to assist in selecting appropriate external knowledge with its inclusion magnitude, preventing the disruption of locality. Simultaneously, in the truthfulness space, we employ the external knowledge to identify a generalizable editing direction to regulate the integrated intrinsic knowledge, alleviating its restriction of generality. Under such a synergistic promotion, extensive experiments show that UniKE achieves promising results under various settings, ensuring that the post-edit MLLM maintains excellent reliability, generality, and locality. Overall, our main contributions are three-fold:• We propose a unified paradigm for multimodal knowledge editing, with both intrinsic and external knowledge represented as vectorized key-value memories, conducting at the same semantic levels in the same transformer layers. • We disentangle the knowledge representations into the semantic and truthfulness spaces, promoting the collaboration between intrinsic knowledge editing and external knowledge resorting. • Our method ensures that, under various backbones and editing scenarios, the post-edit MLLM consistently possesses all three properties well. 
related work: 
 Recent years witness a burgeoning in the techniques of knowledge editing for LLMs [41, 45] , with the post-edit model expected to exhibit three properties [11] : Reliability, Generality, and Locality (Detailed definitions are given in Appendix B). Knowledge editing methods can be divided into two main categories based on the type of knowledge: intrinsic knowledge editing and external knowledge resorting. Intrinsic knowledge editing [6, 28, 25, 26] , involves the parametric storage of knowledge within the model, requiring modifications to LLMs' parameters. While external knowledge resorting [46, 29, 47] typically preserves LLMs' parameters and maintains a knowledge database to retrieve relevant cases for each input with several information retrieval approaches [13, 31] .Overall, intrinsic and external knowledge exhibit significant differences in the knowledge forms (parametric neurons and descriptive in-context examples, respectively).Furthermore, the emergence of MLLMs [1, 48, 19, 33, 40, 8] has sparked several studies on multimodal knowledge editing [4, 30] . However, [4] find that existing methods fall short of expectations when editing MLLMs. Though maintaining high reliability, whether intrinsic knowledge editing or external knowledge resorting, often fails to simultaneously achieve ideal locality and generality as shown in Figure 1 . In this paper, we propose a synthesis of both types of methods for multi-modal editing. By unifying intrinsic and in-context knowledge as vectorized key-value memories, we facilitate collaborative interaction between the two within the unified paradigm, fully utilizing the strengths of each method and enabling the post-edit MLLM to consistently exhibit all three properties well. 
method: 
 In this section, we first develop a unified view for knowledge editing ( §3.1). Within the unified framework, we introduce how to realize intrinsic knowledge editing and external knowledge resorting in the latent space ( §3.2). Finally, we further enhance the collaboration between both types of knowledge to control the overall editing process ( §3.3). The overall framework is shown in Figure 3 .In our general understanding, intrinsic knowledge editing and external (in-context) knowledge resorting seem to have stark differences. In this section, we will demonstrate that both intrinsic and in-context knowledge can be unified as vectorized key-value memories, directly acting on the hidden states within the transformer. Consequently, knowledge editing can be understood as adjusting the key-value pairs in the memory to activate appropriate knowledge for a given query representation.Intrinsic Knowledge as Internal Key-Value Memory. Previous studies have demonstrated that the feed-forward network (FFN) in the transformer harbors a wealth of knowledge [5, 25] . We aim to conduct intrinsic knowledge editing within the FFN and treat FFN as parametric key-value memory storing within the MLLM. Considering a two-layer FFN: given the input latent states, the FFN treats it as a query q, with the first FFN layer acting as keysW f f n K ∈ R d×d ′, and the second layer as valuesW f f n V ∈ R d ′ ×d .Here, d is the hidden dimension of the FFN; and d ′ is the intermediate hidden dimension of the FFN, also interpreted as the memory size. Consequently, FFN effectively uses the query to match the keys, with the intermediate hidden state o representing the weight for each value in memory. FFN then outputs the weighted sum of all values FFN(q). In-context Knowledge as External Key-Value Memory. Traditional in-context knowledge increases the context window space and makes it difficult to quantitatively control [27] . Here we propose a similar view of in-context knowledge as an external vectorized key-value memory to establish its connection with intrinsic knowledge. Specifically, in-context learning typically concatenates the external multimodal knowledge X know with the original input sequence X input to form the combined sequence X = [X know , X input ]. Considering the self-attention mechanism Attn(Q = X, K = X, V = X) in the transformer, during in-context learning, the attention Attn(X input , X, X) for tokens in the original input sequence can actually be formulated as follows:o = Act(qW f f n K + b f f n K ) # Matching keys with query. FFN(q) = oW f f n V + b f f n V #Attn(Xinput, X, X) = α Attn(Xinput, Xinput, Xinput)EQUATIONThe first term h input is the original self-attention output without in-context knowledge. The second term is to treat the hidden states of X know as a key-value memory, the hidden state of X input as the query, selectively activating the relevant in-context knowledge h know . h know then performs position-wise feature shifting on the original attention output to achieve in-context editing, with α as the scaling factor. We give a more complete analysis in Appendix A.Assimilation: Intrinsic Knowledge Editing. As intrinsic knowledge is considered as key-value memory stored within the FFN, akin to [11] , we treat intrinsic knowledge editing as the process of integrating extra knowledge into the internal knowledge memory, thereby establishing connections with prior knowledge. This process is analogous to the Assimilation phase [34] in human cognition, where an individual incorporates new knowledge into their existing cognitive structures. Specifically, based on the analysis in Eq.( 1), the newly added parametric knowledge is stored in the FFN as key-value pairs (the number of new pairs is ne), transforming the output of the FFN as:EQUATIONwhere W extra ) represents the activated weight of the extra value. In this way, the newly injected knowledge, in the form of key-value pairs, is seamlessly integrated into the existing knowledge structure of MLLM.Accommodation: External Knowledge Resorting. As in-context knowledge can also be vectorized into hidden states as external key-value memory, we can interpret in-context knowledge editing as shifting the original post-attention latent states as shown in Eq.( 2), which also allows in-context knowledge to be introduced in a more controlled manner. This process is analogous to the Accommodation phase in human cognition, where individuals modify their existing cognitive schemas to accommodate new information that does not fit into their prior understanding.Assuming the hidden states of in-context knowledge have been extracted and stored as key-value pairs M ext = {(h sem , h pos )}, in the input end, the MLLM is fed only the original sample without concentrating in-context knowledge. Within a given transformer layer, we initially utilize the preattention hidden states h pre input to retrieve the top-K {h sem,i } K i=1 that exhibit the highest cosine similarity from M ext , obtaining the corresponding {h pos,i } K i=1 . As indicated in Eq.( 2), {h pos,i } K i=1 then serves as both keys and values for attention computation, with h pre input acting as the query, thereby achieving the in-context latent states h know . Subsequently, by simply specifying a scalar α, h know is integrated with the original self-attention output h input , acting as a shifting direction that steers the original states closer to the representations of in-context knowledge, thus facilitating editing.Analysis of the Unified Framework. In real life, assimilation and accommodation work together with ongoing interaction to drive cognitive development. Within the unified knowledge editing framework, we also inherently establish a preliminary collaboration between intrinsic knowledge editing and external knowledge resorting: external knowledge assists in storing more generalizable intrinsic knowledge; intrinsic knowledge helps to select appropriate external knowledge. As shown in Figure 3 .a, in the l-th transformer layer, the post-self-attention states following in-context editing, are directly fed into the FFN for intrinsic knowledge editing. when the FFN input integrates generalizable in-context knowledge, the newly added key-value pairs in FFN also tend to store generalizable knowledge to be better activated. Moreover, the output of the FFN, having just undergone intrinsic knowledge editing, is transmitted to the self-attention of the (l + 1) layer. Here, it acts as the query to select suitable hidden states of in-context knowledge for in-context editing. Overall, compared to directly combining different knowledge editing methods with various paradigms, we establish a synergistic correlation with the unification of knowledge editing paradigm, allowing different methods to utilize their strengths to complement each other.To further promote the collaboration between intrinsic knowledge editing and external knowledge resorting, it is essential to emphasize different aspects of knowledge: intrinsic knowledge should prioritize generalizable truthfulness to improve generality, whereas external knowledge should maintain semantic relevance to the input samples to preserve locality. Inspired by this, we extract diverse hidden states for in-context knowledge and innovatively disentangle the knowledge representations into semantic and truthfulness spaces, further enhancing the collaboration within these two spaces.Extracting In-context Knowledge Representations. To construct the representations of in-context knowledge, we first acquire knowledge that the MLLM has not previously mastered, and collect triplets {(Q I , A pos , A neg )}. Q I is the input multimodal question, A pos is the truthful answer, A neg is the MLLM's hallucinated prediction. For each piece of knowledge, we pair Q I + A pos as the positive knowledge, Q I + A neg as the negative knowledge, and separately pass the positive and negative knowledge through the MLLM, obtaining three critical hidden states. Semantic hidden state h sem is related to the last token of the question part before MLLM processes the response, inherently encoding the semantic information on the given examples. Positive hidden state h pos and negative hidden state h neg correspond to the final token of the entire input from the positive and negative knowledge, respectively. They provide insights into how the responses guide the MLLM onto the correct or incorrect track. Note that we store (h sem , h pos ) as the key-value pairs in the knowledge memory for in-context editing in §3.2. More details are given in Appendix C.Disentangling Knowledge Representations. Then we explicitly disentangle the representations of in-context knowledge into semantic and truthfulness spaces. Within the semantic space, h pos and h neg (along with the semantic hidden states h sem ) from the same sample encapsulate identical meanings; whereas in the truthfulness space, h pos and h neg must be distinctly differentiated. Specifically, we introduce a truthfulness encoder Enc T ru (•) and a semantic encoder Enc Sem (•), mapping each pair of {h pos , h neg } to the semantic and truthfulness space, deriving a set of semantic representations (H Sem pos , H Sem neg ) and truthfulness representations (H T ru pos , H T ru neg ), respectively. Within these two latent spaces, we leverage contrastive learning to probe representations with similar truthfulness but different semantics, and conversely, those that are semantically similar but differ in truthfulness. In the truthfulness space, for the given positive or negative truthfulness representations h T ru = h T ru pos,i (h T ru neg,i ), examples sharing the same truthfulness H T ru pos (H T ru neg ) form S + , while those with opposite truthfulness H T ru neg (H T ru pos ) form S -. The objective of contrastive learning is to minimize the distance between h T ru and S + while maximizing the distance between h T ru and S -:EQUATION)where s is the similarity function. In the semantic space, for a given semantic hidden state h sem,i , its corresponding semantic representations (h EQUATIONEnhanced Knowledge Collaboration within Disentangled Spaces. After knowledge disentangling, we could further enhance the knowledge collaboration within the two spaces. Specifically, In the truthfulness space, we calculate the average truthfulness representations ( ĤT ru pos and ĤT ru neg ) over all positive and negative hidden states of in-context knowledge, to regulate intrinsic knowledge editing. As the representations of positive and negative hidden states exhibit distinct truthfulness after training, we identify an editing direction ζ = ĤT ru pos -ĤT ru neg , pointing from the center of untruthful representations to the center of truthful representations. And then we utilize a learnable weight W ζ to map ζ from the truthfulness space back to the representation space: ζ ′ = W ζ ζ. On this basis, during intrinsic knowledge editing in Eq.(3), we further combine W extra V with ζ ′ as follows:EQUATION)where β is an editing scalar. In the semantic space, as we leverage α in Eq.( 2) to control the inclusion magnitude of in-context knowledge, we further leverage the hidden states after intrinsic knowledge editing to adaptively control α. Based on h know , h input in Eq.( 2), we first extract the semantic representations of the injected in-context knowledge h sem know = Enc Sem (h know ) and the hidden states from the last token of h input (h input [-1] serves a similar role as the semantic hidden state h sem ). We then assign the cosine similarity between h sem know and h input [-1] to α , with Eq.( 2) reformulated as:EQUATIONAnalysis of Knowledge Collaboration. In the truthfulness space, ζ is derived from the distribution deviation between hallucinated knowledge and truthful knowledge based on a large number of examples. As the newly integrated intrinsic knowledge is typically learned from a single editing sample which easily leads to overfitting, ζ effectively regulates the values of new intrinsic knowledge into a generalizable truthful direction to improve generality. In the semantic space, when the relevance between the in-context knowledge and the input sample is weak, α = Sim (h sem know ,h input [-1]) adaptively takes a small value thanks to contrastive training. As external knowledge resorting needs to prevent the excessive inclusion of unrelated external knowledge, a smaller α effectively reduces its inclusion magnitude to preserve locality. We further provide quantitative analysis in §4.5.  
experiments: 
 We first evaluate UniKE on one-step editing ( §4.2), the standard setup of multimodal editing. We further extend the setup to sequential editing ( §4.3) and cross-task editing ( §4.4) for evaluation.Dataset & Backbone & baselines. Our experiments are conducted on the MMEdit benchmark [4] , which contains two subtasks: Editing VQA (E-VQA) and Editing Image Caption (E-IC). We leverage Reliability, generality (T-Generality and M-Generality) and locality (T-Locality and M-Locality) as the evaluation metrics. For one-step editing, we conduct experiments on BLIP2-OPT [19] and MiniGPT-4 [48] ; for sequential editing and cross-task editing, we conduct experiments on MiniGPT-4.Furthermore, We use the following baselines: (1) Fine-tuning method: tuning the last layer of MLLM;(2) Intrinsic knowledge editing method: Knowledge Editor (KE) [5] , MEND [28] , T-Patcher [11] ;(3) External knowledge resorting method: In-Context Editing (IKE) [46] , SERAC [29] .Implementation Details. We conduct knowledge editing in the latent space. In intrinsic knowledge editing, we add extra key-value pairs into the last four transformer layers; In external knowledge resorting, we retrieve top-40 in-context hidden states for each case and conduct feature shifting in the last four layers. More details of the experimental setup are shown in Appendix D.Table 1 shows the results of one-step editing, where each edit aims to correct a single mistake. We further provide a statistical summary in Append D. In K-step sequential editing, the model is sequentially edited while encountering mistakes in D edit (|D edit | = K). After the Kth edit, the post-edit MLLM is utilized to evaluate the target properties. Table 2 shows the results of sequential editing (K = 10, 20; we exclude IKE as its setup in sequential editing is meaningless). It can be observed that (i) whether in editing VQA or image captioning tasks, there is a significant decline in the performance of most methods as the number of editing steps (K) increases. Particularly for MEND, while it remains competitive in one-step editing, the results of sequential editing are suboptimal. (ii) The performance of external knowledge resorting (SERAC) is minimally affected by the increase in K. However, it inherently suffers from a lack of locality. (iii) In contrast, our method consistently maintains superior performance compared to the baselines. It consistently outperforms MEND across all metrics of sequential editing, with its advantages over the baseline becoming increasingly significant as K increases.Cross-task editing builds on the foundation of sequential editing (we select K = 10) and requires the MLLM to simultaneously edit VQA and image-caption samples within the same sequence. It is Twitter.It used to be called Twitter, now it's called X.What is the name of his social platform?The social platform of Elon Musk is Twitter.It used to be Twitter, now it's called X.What is the name of this social platform now? Effect of Knowledge Collaboration in Semantic Space. During knowledge collaboration, in semantic space, we adaptively adjust the inclusion magnitude of in-context knowledge (assigning the cosine similarity between h sem know and h input [-1] to α). To demonstrate the superiority of this strategy, we further experiment with several fixed values for α. As shown in Figure 4 .b, the increase in the fixed α enhances the impact of in-context knowledge, which tends to improve generality. However, it also leads to a reduction in locality. In contrast, our method, which adaptively adjusts α based on semantic relevance, customizes an appropriate injection weight for each in-context knowledge. Thereby, we ensure an enhancement in generality while also preventing the disruption to locality. Locality Facebook.To give an intuitive perspective on the disentangled knowledge representations, we employ t-SNE [37] for dimensionality reduction, visualizing the embedding distributions for semantic and truthfulness representations across both positive and negative hidden states. As shown in Figure 5 .a, the positive and negative hidden states display similar distributions in the semantic space, yet are distinctly separated in the truthfulness space. This visualization effectively confirms the efficacy of our approach to knowledge disentangling.Qualitative Examples. As shown in Figure 5 .b and Appendix E, UniKE achieves reliable multimodal editing while generalizing to similar scenarios and ensuring accuracy for irrelevant examples. 
conclusion: 
 In this paper, we introduce UniKE, a multimodal editing framework that establishes a unified paradigm for both intrinsic knowledge editing and external knowledge resorting. We conceptualize both types of knowledge as vectorized key-value memories and effectively enhance their collaborations with knowledge disentangling. Extensive experimental results demonstrate that our method enhances the post-edit MLLMs across various settings (one-step editing, sequential editing, and cross-task editing), ensuring that they maintain excellent reliability, generality, and locality simultaneously.Reliability. Knowledge editing is reliable when the post-edit MLLM successfully changes prediction from y o to y e [11] . We access the reliability based on the average accuracy of the edit case:EQUATIONLocality. Knowledge editing should be implemented locally, ensuring that the post-edit MLLM should not change the output of irrelevant out-of-neighbor examples. And we leverage two metrics to evaluate locality: M T ext loc (T-Locality) and M Img loc (M-Locality). For T-Locality, we remove the visual modules of MLLM, and leverage rudimentary question-and-answer datasets D Loc-T = {(x t , y t )} to examine whether the MLLM's understanding of pure textual input remains unaffected.EQUATIONOf course, we also need to consider the potential ramifications of knowledge editing on visual comprehension. Given D Loc-M = {(v m , x m , y m )}, M-Locality is measured by the rate at which the post-edit MLLM maintains the same predictions as the pre-edit MLLM on multimodal input.EQUATIONGenerality. It is not sufficient for knowledge editing to merely correct individual erroneous inputs.The post-edit MLLM should also generalize to equivalent neighbors with strong generalization [14, 32] . In multimodal scenarios, equivalent neighbors can be rephrased textual sentences or rephrased images, corresponding to the metrics of T-Generality and M-Generality, respectively. And then generality is assessed by the average accuracy on examples uniformly sampled from these equivalent neighbors ( N (x e ) or N (v e )).EQUATIONM Img gen = E (ve,xe,ye)∼D edit (vr )∼N (ve )EQUATION 
 
 Appendix: Previous multi-modal editing focuses on one-step editing, addressing a single error from one target sample at a time, and subsequently evaluating the three metrics based on the sample itself, its equivalent neighbors, and its out-of-neighbor examples. However, one-step editing is not applicable to practical situations. Following [11] , we extend multimodal editing into the setup of sequential editing.In K-step sequential editing, we have a set of target samples D seq to be edited in a sequence (|D seq | = K; each sample in D seq also contains its own equivalent neighbors and out-of-neighbor examples).After continuously editing all K target samples, we obtain a post-edit MLLM f θe (v,x,y)∈Dseq (v,x,y) .We first evaluate the reliability, locality, and generality of the post-edit MLLM within the sequence in a manner similar. And the final performance of the above metrics will be averaged over all D seq ∼ D edit .EQUATIONEQUATIONEQUATIONEQUATIONM ′Img gen = ED seq∼Dedit   1 K(ve,xe,ye)∈Dseq, (vr )∼N (ve) OPT [19] and MiniGPT-4 [48] as the backbone models, which are under BSD 3-Clause License. And the MMEdit benchmark is under MIT license.EQUATIONMoreover, the original setup of MMEdit only involves one-step editing, where each edit aims to correct a single mistake from a single target sample, and the above metrics are assessed after each edit.We further extend the setup to sequential editing and cross-task editing, both of which are defined in the previous section. For sequential editing in E-VQA and E-IC, we select K = 10 and K = 20 as the editing steps within a sequence. For cross-task editing, we choose K = 10, with each editing sequence containing 5 E-VQA samples and 5 E-IC samples. Additionally, we no longer report the results for E-IC and E-VQA separately in cross-task editing; instead, we present the average results of all E-IC and E-VQA samples.Fine-tune. Fine-tuning is the most widely employed strategy for adapting pre-trained language models to specific tasks. So we leverage vanilla fine-tuning as the baseline for multimodal editing. We only tune the last layer of MLLM, which has been verified as the most effective fine-tuning strategy in [4] .Knowledge Editor (KE). KE [6] is a intrinsic knowledge editing method that corrects erroneous knowledge in language models without re-training the whole model. It leverages a hypernetwork (a bidirectional-LSTM) to predict the weight update for constrained optimization.MEND. Model Editor Networks with Gradient Decomposition (MEND [28] ) is also a method of intrinsic knowledge editing. It learns to transform the editing gradients into a generalizable direction via employing a low-rank decomposition of gradients, aiming to keep both generality and locality during knowledge editing.T-Patcher. T-Patcher [11] is also a typical intrinsic knowledge editing method that integrates addition neurons for addressing mistakes in the last several layers of the Feed-Forward Network (FFN) within language models.In-context Knowledge Editing (IKE). IKE [46] edits the language model by prompting the model with several retrieved edit demonstrations from the external database. As a result, the language model can generate outputs that align with the provided knowledge when given a refined knowledge context as a prompt.SERAC. SERAC [29] is also a method of external knowledge resorting. It leverages an explicit memory system to cache edits, which is later utilized to adjust the output of the language model during inference. Moreover, the memory system employs a small auxiliary scope classifier to determine whether the input falls within the scope of the memory cache.We conduct knowledge editing in the latent space with a unified paradigm. In intrinsic knowledge editing, we add extra 10 key-value pairs in the FFN of the last four transformer layers; for external Reliability It is Twitter.It used to be called Twitter, now it's called X.What is the name of his social platform?The social platform of Elon Musk is Twitter. This is a train.This is an old-fashioned tram.What is the speed of this?one-step edit takes only a matter of seconds and we run all experiments with 6 NVIDIA RTX A6000 GPUs.In Table 5 , we present a statistical summary of the average reliability, locality, and generality of the post-edit MLLM for both one-step and sequential editing. Although some methods (e.g., MEND) achieve reliable editing in multimodal scenarios, UniKE effectively balances all three target properties, significantly outperforming baseline methods in terms of generality and locality.In Figure 7 , we show more qualitative examples. In case (a), after altering the MLLM's cognitive bias of a particular concept through knowledge editing, we enable the MLLM to leverage its existing world knowledge to automatically correct other related details associated with that concept. In cases (b) (c), after updating outdated knowledge through UniKE, the MLLM can activate this updated knowledge to correctly answer a series of related questions. In cases (d) and (e), model editing enables the MLLM to look beyond the superficial aspects and answer questions from a deeper perspective. Furthermore, for all five cases, we ensure the locality of the post-edit MLLM without altering its original responses to irrelevant inputs.There still exist some limitations in our work: (1) At present, we have only considered editing MLLMs with visual comprehension capabilities, and have not addressed editing in visual generation scenarios.(2) Due to the resource limitation, we do not afford to edit MLLMs with a larger number of parameters such as the 65B LLaMA Adapter V2 [10] .Ethical Impacts. This study does not raise any ethical concerns. The research does not involve subjective assessments or the use of private data. Only publicly available datasets and models are utilized for experimentation.Expected Societal Implications. This study proposes a data-and time-efficient way to edit MLLMs. A major societal concern with this technology lies in its potential for misuse. For example, some malicious individuals may exploit our technology to fabricate false information for knowledge editing and spread rumors. To counter these threats, it is crucial to develop strong ethical standards and implement ongoing surveillance.Justification: The codes are provided in supplemental materials (also in the anonymous repository). The benchmark data comes from the open-source dataset, MMEdit. Guidelines:• The answer NA means that paper does not include experiments requiring code. Guidelines:• The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.• If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).10. 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Decision: All reviewers acknowledged the novelty of this paper, as well as the solid and thorough experiments conducted. In the initial reviews, there were suggestions to further demonstrate the superiority of the proposed method from various perspectives. After rebuttal phase, the paper received positive feedback from all reviewers. AC has also carefully reviewed the paper, the reviews, and the rebuttal, and concurs with the reviewers' opinions. Therefore, AC recommends acceptance. It would be greatly beneficial to include the additional results from the rebuttal phase in the final version.

Reviews: review: paper_summary: paper_summary: This paper proposes UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Within such a unified framework, the authors further promote knowledge collaboration by disentangling the knowledge representations into the semantic and truthfulness spaces. Extensive experiments validate the effectiveness of UniKE, which ensures that the post-edit MLLM simultaneously maintains excellent reliability, generality, and locality.
strength_weakness: strength_weakness: 1. The authors only edit Qformer style MLLMs (MiniGPT4, BLIP2), more foundation models should be compared. e.g., LLava. Besides, the improvement on BLIP2 (Tab.1) seems incremental.
2. More recent model editing baselines should be compared to prove the effectiveness of the proposed methods.
3. The paper focuses on MLLM editing, what are the main differences between MLLM editing and LLM editing? What are the specific designs for multimodal models? I notice that some compared methods are proposed for LLM editing. Can the proposed methods be used on LLM editing? The authors should give some explanations and experimental results if possible.
questions: questions: Please refer to the weakness below.
limitations: limitations: The paper briefly mentioned limitations.
paper_summary: paper_summary: UniKE is a unified framework for multi-modal knowledge editing that includes three main aspects:
1.Knowledge Separation: UniKE divides knowledge into factuality and semantic spaces to manage and coordinate different types of knowledge more effectively.
2.Knowledge Collaboration: In the factuality space, UniKE standardizes new knowledge based on a learned factuality distribution, enhancing reliability and generality. In the semantic space, it adjusts the integration of external knowledge based on relevance to the input samples, maintaining locality.
3.Multi-step Editing: UniKE supports single-step, multi-step sequence, and cross-task editing while maintaining high reliability, generality, and locality.
Through these innovations, UniKE significantly improves performance in multi-modal knowledge editing tasks.
strength_weakness: strength_weakness: 1.The paper does compare UniKE with other intrinsic knowledge editing and external knowledge resorting methods and highlights its efficiency in several aspects. However, it lacks a detailed discussion on computational speed and resource utilization.

2.The reliance on fine-tuning for knowledge updates could lead to overfitting, especially if the model is frequently updated. This could impact the model’s generalization abilities, making it less effective in unforeseen or less frequent scenarios.

3.The paper lacks detailed explanations for the evaluation metrics in Line 240. In Table 2, across multiple experiments, it is unclear why there is little difference compared to the SERAC method in the T-Loc metric, but a significant difference in the M-Loc metric.

4.The paper lacks detailed information about the parameter "n" used in the contrastive learning formula, making it difficult to understand its impact on model performance, and does not discuss how different values of "n" might influence the results and effectiveness of the method.
questions: questions: Please address the comments in the weaknesses section.
limitations: limitations: The limitations of the article have been discussed by the authors
paper_summary: paper_summary: This paper proposes UniKE, a novel multimodal editing method that establishes a unified perspective for intrinsic knowledge editing and external knowledge resorting. On this basis, the authors combine both types of knowledge editing methods, executing them in the latent space with a unified paradigm. Furthermore, this paper proposes to disentangle the knowledge representations into the semantic and truthfulness spaces, effectively enhancing the collaboration between intrinsic knowledge and external knowledge resorting. Extensive experimental results show that UniKE achieves promising results under various settings, ensuring that the post-edit MLLM maintains excellent reliability, generality, and locality.
strength_weakness: strength_weakness: (1) In the NLP community, some studies will discuss the resilience to overediting [1] of knowledge editing methods by adopting the contrastive knowledge assessment [2] (CKA). Unlike the locality property that measures whether LLMs forget previous knowledge, overediting can be understood as excessive generalized to seemingly similar but unrelated samples. Although there may be no current work on multimodal editing that discusses the phenomenon of overediting, I encourage authors to add relevant experiments for a straightforward comparison of the resilience to overediting among each method (UniKE, MEND, T-Patcher, and IKE).
(2) A more challenging task of knowledge editing is counterfactual editing, where the edited answer $y$ to the question $x$ can sometimes be counterfactual to the real world. A typical counterfactual editing dataset in the NLP community is called COUNTERFACT [3], which more accurately reflects the true effectiveness of knowledge editing methods by avoiding the effects of LLMs knowing this knowledge before editing. I encourage authors to construct multimodal counterfactual editing datasets and conduct more experiments to verify whether UniKE performs better in counterfactual editing scenarios compared to MEND, T-Patcher and IKE. 
[1] Zheng, Ce, et al. "Can we edit factual knowledge by in-context learning?." arXiv preprint arXiv:2305.12740 (2023).
[2] Dong, Qingxiu, et al. "Calibrating factual knowledge in pretrained language models." arXiv preprint arXiv:2210.03329 (2022).
[3] Meng, Kevin, et al. "Locating and editing factual associations in GPT." Advances in Neural Information Processing Systems 35 (2022): 17359-17372.
questions: questions: See the weaknesses.
limitations: limitations: The authors have adequately discussed the limitations and potential negative societal impact.
paper_summary: paper_summary: The paper introduces UniKE, a novel multimodal editing method that addresses challenges in knowledge editing for Multimodal Large Language Models. UniKE unifies intrinsic knowledge editing and external knowledge resorting by vectorized key-value memories. By disentangling knowledge representations into semantic and truthfulness spaces, UniKE promotes collaboration between intrinsic and external knowledge editing, enhancing the post-edit MLLM's reliability, generality, and locality. Extensive experiments validate UniKE's effectiveness.
strength_weakness: strength_weakness: 1.	The presentation is confusing. For example, in intrinsic knowledge editing, how do you actual edit the intrinsic knowledge in FFN. Do you follow the same pipeline as T-Patcher or adding an additional neural network? Although intrinsic knowledge is considered as key-value pairs but they are different in the end.
2.	The unified framework and disentanglement of knowledge representations into semantic and truthfulness spaces introduces additional complexity to the editing process. If I understand correctly from the implementation details, it requires more than 15k additional triplets to train the encoders.
3.	In the experiments, I think /beta is another hyperparameter, but I did not find an experiment discussing it.
questions: questions: Why would you add extra 10 key-value pairs in the FFN of the last four transformer layers in intrinsic knowledge editing?
It seems that \zeta is quite sensitive. Could you explain that?
limitations: limitations: N/A

Comments: title: Official Comment
Tables 1-6 in the Rebuttal PDF
Table 7 and Figure 2 in the Rebuttal PDF
Tables 7, 10 and Figure 1 in the Rebuttal PDF
Clarification on Weakness
comment: Thank you for raising the score. We deeply appreciate your recognition of our work and the constructive advice you've offered!
Thank you for your rebuttal response. I think that over-editing evaluation and counterfactual editing are critical tasks that can reflect whether a model truly possesses the capabilities of knowledge cognitive learning in knowledge editing scenarios. It's impressive to see that UniKE performs well on these two challenging tasks. I consider UniKE to be a strong contribution, and I will raise my score to 9.
Thank you for raising the score. Your valuable suggestions greatly contribute to the quality of our paper.  And we are deeply grateful for your insightful suggestions on our work!
The rebuttal have addressed most of my concerns. I will raise my score to 6.
Thank you for raising the score. Your valuable suggestions greatly contribute to the quality of our manuscript. Thank you again for your precious time and valuable suggestions!
Thanks for your response. The Rebuttal addresses most of my concerns. I will raise my score to 7.
To facilitate your reading, we also paste our additional experimental results here, **which are consistent with the tables in the rebuttal PDF**.

&nbsp;

**Table 1**: Performance of one-step editing on LLaVA1.5 (We average the results on E-IC and E-VQA).

| Method | Rel. | T-Gen. | M-Gen. | T-Loc. | M-Loc. | **Avg** |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| FT | 67.4 | 57.9 | 54.8 | 67.1 | 63.2 | 62.1 |
| KE | 72.7 | 65.4 | 55.3 | 82.2 | 67.3 | 68.4 |
| T-Patcher | 89.0 | 76.5 | 69.0 | 81.2 | 81.3 | 79.6 |
| MEND | 95.4 | 92.6 | 78.3 | 83.5 | 80.3 | 86.0 |
| IKE | 93.4 | 85.1 | 77.9 | 27.7 | 3.2 | 57.5 |
| SERAC | **96.3** | 92.4 | 85.5 | 83.3 | 7.7 | 73.0 |
| **UniKE** | 95.7 | **92.8** | **88.4** | **86.0** | **86.4** | **89.9** |

&nbsp;

**Table 2**: Performance of cross-task editing on LLaVA1.5.

| Method | Rel. | T-Gen. | M-Gen. | T-Loc. | M-Loc. | Avg |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| FT | 66.9 | 57.2 | 51.0 | 62.3 | 54.4 | 56.4 |
| KE | 69.8 | 60.3 | 52.5 | 79.3 | 62.1 | 64.8 |
| T-Patcher | 81.2 | 60.0 | 57.4 | 77.4 | 76.5 | 70.5 |
| MEND | 90.4 | 84.3 | 73.8 | 78.6 | 76.0 | 80.6 |
| SERAC | 92.1 | 88.3 | 82.5 | 82.2 | 1.2 | 69.3 |
| **UniKE** | **92.2** | **89.2** | **83.8** | **82.7** | **84.7** | **86.5** |

&nbsp;

**Table 3**: Performance of cross-task editing on BLIP-2 OPT.
| Method | Rel. | T-Gen. | M-Gen. | T-Loc. | M-Loc. | Avg |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| FT | 57.2 | 49.9 | 43.2 | 52.2 | 49.7 | 50.4 |
| KE | 64.2 | 60.1 | 57.2 | 83.5 | 59.2 | 64.8 |
| T-Patcher | 83.1 | 69.7 | 65.9 | 84.5 | 77.9 | 76.2 |
| MEND | 84.2 | 82.4 | 74.9 | 91.4 | 80.2 | 82.6 |
| SERAC | 90.8 | 89.2 | 84.1 | 90.0 | 1.7 | 71.2 |
| **UniKE** | **91.1** | **90.6** | **88.2** | **91.7** | **85.6** | **89.4** |

&nbsp;

**Table 4**: Comparison with recent baselines for one-step editing on MiniGPT-4 (We average the results on E-IC and E-VQA).
| Method | Rel. | T-Gen. | M-Gen. | T-Loc. | M-Loc. | Avg |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| MENMET | 97.0 | 96.2 | 82.4 | 98.0 | 85.2 | 91.8 |
| WISE | 97.2 | 92.2 | 88.7 | 98.4 | **88.2** | 93.0 |
| **UniKE** | **97.4** | **96.6** | **92.6** | **98.8** | 88.1 | **94.7** |

&nbsp;

**Table 5**: Comparison with recent baselines for cross-task editing on MiniGPT-4.
| Method | Rel. | T-Gen. | M-Gen. | T-Loc. | M-Loc. | **Avg** |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| MENMET | 88.4 | 87.2 | 78.0 | 86.1 | 82.5 | 84.4 |
| WISE | 89.2 | 85.4 | 83.4 | 87.8 | 83.6 | 85.9 |
| **UniKE** | **90.7** | **88.2** | **86.8** | **90.4** | **83.8** | **88.0** |

&nbsp;

**Table 6**: Performance of each method on LLM editing task (ZsRE) for one-step editing and 200-step editing.
|  | ONE-STEP EDITING | | ||200-STEP EDITING | | | |
| :---   | :---:| :---: | :---:  | :---: | :---: |  :---: | :---: |  :---: | 
| **Method** |**Rel.** | **Gen.** | **Loc.** | **Avg.** | **Rel.** | **Gen.** | **Loc.** | **Avg.** |
| FT  | 77.4 | 76.7 | 35.5 | 63.2 | 19.5 | 17.2 | 5.4 | 14.0
| KE  | 20.6 | 20.1 | 81.3 | 40.7 | 7.6 | 6.8 | 65.8 |  26.7
| T-Patcher  | 97.1 | 95.0 | 96.2 | 96.1 | 81.4 | 70.6 | 91.3 | 81.1
| MEND  | 98.2 | 97.7 | 97.4 | 97.8 | 0.0 | 0.0 | 0.0 | 0.0 |
| In-Context Editing  | 99.4 | 97.2 | 59.2 | 85.3 | - | - | - | - | 
| SERAC  | 88.6  | 87.9 | 99.9 | 92.1 | 24.0 | 23.2 | **96.4** | 47.9
| MENMET  | 99.1 | 86.8 | 97.4 | 94.4 | 82.9 | 73.6 | 90.2 | 82.2
| WISE  | 98.8 | 96.3 | **99.9** | 98.3 | 82.8 | 74.7 | 95.5 | 84.3
| **UniKE**  | **99.5** | **97.9** | 99.6 | **99.0** | **85.1** |  **76.7** | 95.6 | **85.8** |

&nbsp;

These experiments will be integrated into the main body or the appendix of our paper. We hope we have addressed all of your concerns. Discussions are always open. Thank you again for your time and valuable suggestions!
To facilitate your reading, we also paste our additional experimental results here, **which are consistent with the tables / figures in the rebuttal PDF**.

&nbsp;

**Table 7:** The computational speed, resource utilization and performance of each method. We use the average results of five metrics (Reliability, T-Generality, M-Generality, T-Locality, and M-Locality) as the performance measure.
| Method    | GPU memory | Editing time for each sample | Avg performance |
|:----------|:----------:|:----------------------------:|:---------------:|
| FT        | 22G        | 6.1s                         | 60.6            |
| KE        | 24G        | 5.8s                         | 74.7            |
| T-Patcher | 18G        | 4.7s                         | 80.4            |
| MEND      | 36G        | 5.2s                         | 90.3            |
| IKE       | 20G        | 1.6s                         | 65.5            |
| SERAC     | 49G        | 3.6s                         | 76.4            |
| **UniKE**     | 18G        | 5.0s                         | **95.2**        |

&nbsp;

**Table corresponding to Figure 2 in Rebuttal PDF:** Editing performance on different value of $n$. Generality is the average result of T-Generality and M-Generality; while Locality is the average result of T-Locality and M-Locality.
| $n$ | 0 | 4000 | 8000 | 12000 | 16000 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| Rel. | 96.2 | 97.0 | 97.8 | 98.1 | 98.0 |
| Gen. | 91.2 | 93.3 | 94.5 | 94.9 | 95.1 |
| Loc. | 90.3 | 92.2 | 93.3 | 93.7 | 93.8 |

&nbsp;

We hope we have addressed all of your concerns. Discussions are always open.  Thank you once again for your constructive suggestions!
To facilitate your reading, we also paste our additional experimental results here, **which are consistent with the tables / figures in the rebuttal PDF**.

&nbsp;

**Table 7:** The computational speed, resource utilization and performance of each method. We use the average results of five metrics (Reliability, T-Generality, M-Generality, T-Locality, and M-Locality) as the performance measure.
| Method    | GPU memory | Editing time for each sample | Avg performance |
|:----------|:----------:|:----------------------------:|:---------------:|
| FT        | 22G        | 6.1s                         | 60.6            |
| KE        | 24G        | 5.8s                         | 74.7            |
| T-Patcher | 18G        | 4.7s                         | 80.4            |
| MEND      | 36G        | 5.2s                         | 90.3            |
| IKE       | 20G        | 1.6s                         | 65.5            |
| SERAC     | 49G        | 3.6s                         | 76.4            |
| **UniKE**     | 18G        | 5.0s                         | **95.2**        |

&nbsp;

**Table 10:** Editing time cost and performance with/without encoders for UniKE. The time refers to the average editing or inference time for one sample. Gen is the average result of T-Generality and M-Generality; while Loc is the average result of T-Locality and M-Locality.

| Method | GPU Memory | Editing time | Inference time | Rel. | Gen. | Loc. |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
| w/o encoders | 17.7GB | 4.92s | 0.212s | 96.2 | 91.2 | 90.3 |
| UniKE | 17.8GB | 5.04s | 0.217s | **97.4** | **94.6** | **93.5** |

&nbsp;

**Table corresponding to Figure 1 in Rebuttal PDF:**  Editing Performance on Different Value of $\beta$. Generality is the average result of T-Generality and M-Generality; while Locality is the average result of T-Locality and M-Locality.
| $\beta$ | 0.0 | 0.4 | 0.8 | 1.0 | 1.2 | 1.6 |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | 
| Rel. | 97.0 | 97.7 | 97.8 | 98.0 | 97.5 | 97.9 |
| Gen. | 91.7 | 93.3 | 95.0 | 95.1 | 94.8 | 94.7 |
| Loc. | 91.6 | 93.0 | 93.6 | 93.8 | 93.2 | 93.5 |

&nbsp;

We hope we have effectively addressed your concerns. Discussions are always open. Thank you once again for your time and insightful comments!
I apologize for the mistake in my previous feedback. I have reviewed my comments and provided the correct feedback on the weaknesses of your paper. I hope this clarifies my observations and helps you improve your paper.
Dear Reviewer:
﻿
Thank you very much for your kind feedback and valuable comments. I have carefully read your comments. However, it appears that the content from the "Strengths" section may have been inadvertently pasted into the "Weaknesses" section. As a result, the weaknesses you mentioned still seem to describe the strengths of our work. This makes it challenging to identify and address the specific concerns you may have intended to highlight. ﻿
﻿
Could you please review your comments once more and provide additional clarification on the weaknesses you observed? Your insights are crucial for improving our paper, and I am keen to address all your concerns effectively. ﻿
﻿
Thank you again for your time and valuable feedback!