Title: Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
Venue: NeurIPS 2024
Accepted: yes
Keywords: vision and language

multimodal understanding
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.

Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration

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 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 postedit MLLM simultaneously maintains excellent reliability, generality, and locality. The code for UniKE is available at https://github.com/beepkh/UniKE .

1 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

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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.

2 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.

3 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.

3.1 A Unified View for Knowledge Editing

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

Original

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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 keys W ffn K ∈ R d × d ′ , and the second layer as values W ffn 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 ) .

where b ffn ∈ R d ′ , and b ffn ∈ R d are two bias vectors. Act ( · )

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:

The 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.

3.2 Unified Knowledge Editing within Latent Space

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:

where W extra K ∈ R d × ne and W extra V ∈ R ne × d are the extra keys and values, b extra K ∈ R ne is an extra bias vector. o extra = Act ( q · W extra K + b extra K ) 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.

3.3 Enhanced Collaboration with Knowledge Disentangling

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 Tru ( · ) 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 Tru pos , H Tru 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 Tru = h Tru pos,i ( h Tru neg,i ), examples sharing the same truthfulness H Tru pos ( H Tru neg ) form S + , while those with opposite truthfulness H Tru neg ( H Tru pos ) form S -. The objective of contrastive learning is to minimize the distance between h Tru and S + while maximizing the distance between h Tru and S -:

where s is the similarity function. In the semantic space , for a given semantic hidden state h sem,i , its corresponding semantic representations ( h Sem pos,i and h Sem neg,i ) form the S + , while those from other examples H Sem pos \ h Sem pos,i , H Sem neg \ h Sem neg,i form S -. And the loss of contrastive learning is:

Enhanced 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 ( ˆ H Tru pos and ˆ H Tru 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 = ˆ H Tru pos -ˆ H Tru 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:

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:

Analysis 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.

4 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.

4.1 Experimental Setup

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.

4.2 Main Results on One-step Editing

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.4. We have the following observations: (i) Most knowledge editing methods could achieve desirable reliability. (ii) Despite achieving high locality, most intrinsic knowledge editing methods have room for improvement in generality ( e.g. , average generality and locality of T-Patcher across all settings are 71.6 and 87.2). (iii) Although achieving commendable generality, the locality of external knowledge resorting methods is not ideal . Specifically, the average locality (generality) of IKE and SERAC are 26.8 (88.6) and 52.5(91.3), respectively. (iv) Our method effectively balances all three target properties, outperforming the previous SOTA method, MEND . Compared to MEND which transforms the gradients of knowledge editing to a generalizable direction for keeping both locality and generality, UniKE significantly achieves superior locality (93.8 vs. 89.4 on average) and generality (95.1 vs. 88.6 on average).

(a) Results on 10-step editing on VQA.

(b) Results on 20-step editing on VQA.

4.3 Main Results on Sequential Editing

In K -step sequential editing, the model is sequentially edited while encountering mistakes in D edit ( |D edit | = K ) . After the K th 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.

4.4 Main Results on Cross-task Editing

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.

Table 3 presents the results of cross-task editing (averaging the results over all E-VQA and E-IC samples). It is evident that most baselines struggle to effectively edit both tasks within a single editing sequence. In contrast, UniKE excels at integrating the knowledge from these two distinct tasks, significantly outperforming baseline methods in terms of reliability, generality, and locality.

4.5 In-Depth Analysis

Effect of Individual Components. We investigate the effectiveness of each component and conduct the following experiments on one-step editing: (1) only Intrin & only Latent-IKE: We utilize either intrinsic knowledge editing or external knowledge resorting (Latent IKE), conducting multimodal editing in the latent space. In Rows 1 and 2 of Table 4, it is evident that single-type knowledge editing approaches cannot simultaneously possess all three properties well, resulting in either generality or locality being unsatisfactory. (2) Intrin + IKE: We simply combine intrinsic knowledge editing and vanilla in-context editing without paradigm unification. The results in Row 3 demonstrate that if

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Twitter, now it's called X.

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integrating both types of knowledge editing methods without a unified paradigm, it is difficult to fully leverage the individual advantages of each method, still leading to suboptimal generality and locality. (3) Intrin + Latent IKE: We remove the enhanced knowledge collaboration proposed in §3.3. The results in Row 5 validate that the enhanced collaboration based on knowledge representation disentangling further enables the post-edit MLLM to achieve superior generality and locality.

Effect of In-context Editing in Latent Space. To validate the superiority of in-context editing in latent space, in Figure 4.a we compare Latent IKE with vanilla IKE across different numbers of in-context samples/hidden-states (both combined with intrinsic knowledge editing without enhanced knowledge collaboration). IKE is criticized that in-context samples are difficult to quantitatively control and take up context window space [27, 23]. It can be observed that in IKE, as the number of in-context samples increases, though generality generally trends upward, there is a notable decline in locality. While in our method, via quantitatively controlling the inclusion of in-context hidden states while also reducing the prompt length, both generality and locality of the post-edit MLLM show an overall upward trend as the number of in-context samples increases .

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.

Effect of Knowledge Collaboration in Truthfulness Space. During knowledge collaboration, in the truthfulness space, we identify a truthful editing direction, ζ to guide intrinsic knowledge editing. To assess the effects of ζ , we conduct the following experiments: (1) w/o ζ : removing the regulation of ζ as per Eq.(3); (2) random ζ : replacing ζ with a random tensor; (3) semantic ζ : generating ζ in the same manner within the semantic space. As depicted in Figure 4.c, ζ could further enhance the generalizability of intrinsic knowledge, thus achieving superior editing performance. However, when ζ does not point towards the correct editing direction (random or semantic ζ ), it acts as a disruptor, thereby impairing the editing performance compared to w/o ζ .

Visualization of Different Knowledge Spaces. 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.

5 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.

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Appendix

A In-context Knowledge as External Key-Value Memory

In §3.1, we provide a brief analysis of how in-context knowledge can be considered as an external vectorized key-value memory, and how to conduct in-context editing as feature shifting in the latent space. Here, we present a more detailed derivation of Eq.(2). 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,K,V ) = softmax ( QW q W ⊤ k K ⊤ ) V W v in the transformer ( W k , W q , W v denote the learnable key, query, and value matrices, respectively), the in-context knowledge simply changes the attention module through prepending a context matrix before the original input examples. During in-context learning, the attention Attn ( Q = X input , K = X,V = X ) for tokens in the original input sequence can actually be formulated as follows:

Here α is a scalar that represents the sum of normalized attention weights between in-context knowledge and the original input:

where x input is the token within the original input sequence. We can find that in-context learning actually applies a position-wise modification to the original attention output by shifting the original output features, with the self-attention controlling the shift direction and α controlling the shift distance. Therefore, in this paper, we dynamically adjust the value of α to control the inclusion magnitude of in-context knowledge representations.

B Task Definition

B.1 Multimodal Knowledge Editing

The goal of multimodal knowledge editing is to efficiently modify an initial MLLM's behavior based on a specific edit descriptor, without incurring significant retraining costs or affecting its behavior on other unrelated samples. Formally, ( v e , x e , y e ) ∈ D edit is the edit descriptor, where v e refers to the visual input, x e refers to the textual input, y e denotes the desired output. And we represent the MLLM (with its parameters denoted as θ ) as a function f : ( V , X ) -→ Y that maps the multimodal input ( v, x ) to its corresponding prediction y o = f θ ( v, x ) . For intrinsic knowledge editing, the parameters of the post-edit MLLM f post are updated to θ e (( v e , x e , y e )) ; while for external knowledge resorting methods, the MLLM's parameters remain θ , and additional relevant external knowledge K is incorporated as the extra input. On this basis, a successful edit should first adjust the MLLM's output on the input ( v e , x e ) from y o to y e . Additionally, there is a broad set of inputs closely associated with the edit descriptor, referred to as the editing neighbor N ( v e , x e ) . The MLLM's behavior should also be corrected for examples within this neighbor while maintaining its performance for out-of-neighbor examples unaltered:

Based on the above analysis, there are often three metrics used to measure the performance of the post-edit MLLM: reliability, locality, and generality.

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:

Locality. 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 Text 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.

Of 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.

Generality. 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 ) ).

(

B.2 Sequential Editing

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 θ e ( ∑ ( v,x,y ) ) .

v,x,y

)

∈D

seq

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 .

B.3 Cross-Task Editing

On the basis of sequential editing, we further allow the K target samples within a sequence to come from different tasks. For example, a single sequence might include samples from both the VQA task and the image caption task. This requires the MLLM to integrate knowledge from distinct tasks to achieve cross-task editing. The evaluation for the above metrics is similar to that of sequential editing. However, instead of separately reporting metrics for each task, we mix the data from different tasks and calculate a single set of (Reliability, T-Locality, M-Locality, T-Generality, M-Generality) for the mixed data. This provides a measure of the post-edit MLLM's average performance across different tasks.

C Preparing In-context Knowledge for Representation Extraction

To extract representations of in-context knowledge, we should first prepare a set of in-context knowledge for the MLLM. In multimodal scenarios [18, 17, 44, 2, 9, 51, 49, 39, 21, 20, 22, 12, 38, 7], We aim to provide in-context knowledge that the MLLM has not previously mastered, which means the MLLM cannot provide correct answers to the corresponding questions.

And we attribute MLLMs' hallucinated responses to deficiencies at three levels, as shown in Figure 6: (1) Insufficient vision extraction. MLLMs first utilizes a Visual Prompt Generator (VPG, e.g. , Qformer [19]) to abstract image features [48]. However, some necessary reasoningaware visual details that complement the primary content and semantically connect the text instructions, may be ignored and not extracted by the VPG [16, 15]. (2) Inaccurate vision recognition. Even if the VPG extracts sufficient visual features for reasoning, the MLLM might fail to understand the corresponding visual objects if it has not been sufficiently exposed to these feature patterns during training, resulting in inaccurate recognition of visual objects [24, 43]. (3) Incorrect text-vision collaborative reasoning. Even with sufficient vision extraction and accurate vision recognition, MLLMs may struggle with understanding the

Right: Red.

Wrong: Blue.

Question:What color is the car

coming towards us in the distance

Right: Owl.

Wrong: Sparrow.

Question:What kind of animal is this?

Right: Inter Miami CF.

Wrong: Paris Saint-Germain

Question:Which soccer club does

this player currently play for?

Image

Visual Objects that

MLLMs focus on

Image

Image

Visual Objects that

MLLMs focus on

Visual Objects that

MLLMs focus on

Level I: Insufficient vision extraction

Level II: Inaccurate vision recognition

Level III: Incorrect text-vision

collaborative reasoning

spatial relationships between different visual regions. Moreover, they may exhibit errors in commonsense knowledge when combining vision with text instructions, ultimately resulting in incorrect text-vision collaborative reasoning [42].

On this basis, we collect a substantial set of hallucinated predictions from MLLMs across these three levels to develop in-context knowledge. For each multimodal question Q I with MLLMs' hallucinated predictions A neg , we additionally provide a truthful answer A pos . For each piece of knowledge, we pair Q I + A neg as the negative knowledge and Q I + A pos as the positive knowledge. For example, assuming [IMG] is an image of Lionel Messi, '[IMG] Question: Which soccer club is this player currently playing for? Answer: Paris Saint-Germain. ' represents the negative knowledge; while '[IMG] Question: Which soccer club is this player currently playing for? Answer: Inter Miami. ' represents the positive knowledge. Subsequently, as stated in §3.3, we can extract several critical hidden states for the collected in-context knowledge.

D Experimental Details

D.1 Dataset

We conduct experiments on the MMEdit benchmark [4], which consists of two sub-tasks: Editing VQA (E-VQA) and Editing Image Caption (E-IC). We leverage Reliability, locality (T-Locality and M-Locality), and Generality (T-Generality and M-Generality) as the evaluation metrics. The definitions of each metric are given in the previous section (Appendix B). We leverage BLIP2-

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.

Moreover, 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.

D.2 Baselines

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.

D.3 Implementation Details

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

Before Editing

It used to be called

Twitter, now it's called X.

After Editing

What is the

name of his

social

platform?

Generality

The social platform of Elon

now it's called X.

What is the

name of this

social

platform now?

Locality

Facebook.

Facebook.

What is the

name of this

social

platform now?

Before Editing

After Editing

Before Editing

After Editing

Reliability

This is a train.

Before Editing

This is an old-fashioned tram.

After Editing

What is the

speed of

this?

Generality

The train usually runs at

100-160 kms per hour.

The old-fashioned tram runs

at 50 to 70 kms per hour

What's on

the road?

Locality

A motorcycle

What is in

the picture?

Before Editing

After Editing

Before Editing

After Editing

A motorcycle

Reliability

The antelope entered the

lion's territory

Before Editing

Lions usually choose

smaller and slower prey.

After Editing

Why doesn't

the lion chase

the bear?

Generality

Because they live together.

Lions usually choose smaller

and slower prey.

What is the

person doing?

Locality

Feeding a dog.

Why is the

lion chasing

the antelope?

Before Editing

After Editing

Before Editing

After Editing

Feeding a dog.

Reliability

Seagulls eat fish.

Before Editing

Seagulls may look for

food such as ice cream.

After Editing

Why did the

seagull fly to

this person?

Generality

The seagull looks happy

The seagull wants to

eat the ice cream.

What does

this animal

usually eat?

Locality

Grass, leaves, fruits

and other plants

What does

this animal

feed on?

Before Editing

After Editing

Before Editing

After Editing

Grass, leaves, fruits

and other plants

Reliability

Android 12.

Before Editing

Android 14.

After Editing

What is the

latest system

for this

company's

mobile phones?

Generality

Android 12.

Android 14 is available.

What is the

latest version

of this

company's OS?

Locality

What is the

latest version

of this OS?

Before Editing

After Editing

Windows 11.

Before Editing

After Editing

Windows 11.

(a)

(c)

(b)

(d)

(e)

Q

鱼

G

knowledge resorting, we retrieve top-40 hidden states of in-context knowledge with the highest similarity for each case and conduct feature shifting for in-context editing in the last four transformer layers. To extract the hidden states of in-context knowledge, we collect more than 15K triplets. Furthermore, during knowledge disentangling, both the truthfulness encoder and the semantic encoder simply consist of several MLP layers. We leverage the collected in-context knowledge representations to pre-train the encoders via contrastive learning [50]. During contrastive learning, both encoders are optimized using the Adam optimizer with a learning rate of 1e-4. After that, completing a single

one-step edit takes only a matter of seconds and we run all experiments with 6 NVIDIA RTX A6000 GPUs.

D.4 Statistical Summary of Results

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.

E Qualitative Examples

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) and (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.

F Limitations

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].

G Broader Impacts

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.

NeurIPS Paper Checklist

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Peer reviews:
comments: 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.

STRENGTHS:
1.	UniKE establishes a unified framework for intrinsic knowledge editing and external knowledge resorting.
2.	Extensive experiments demonstrate that UniKE consistently maintains excellent reliability, generality, and locality

WEAKNESSES:
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:
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:
N/A
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-06

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n(1) I think knowledge editing for MLLMs is a relatively new topic. Previously, a few studies merely adapted existing knowledge editing methods from the NLP field into multimodal domain. To the best of my knowledge, this paper is the first to conduct a detailed and systematic analysis of the strengths and weaknesses of existing methods when applied to editing multimodal LLMs.\n\n(2) The proposed method is very novel and effective. Previous efforts in knowledge editing show significant differences between intrinsic knowledge editing methods and external knowledge resorting methods. In this work, the authors ingeniously convert in-context editing into the format of feature shifting, achieving a unification of the editing paradigms that can operate simultaneously within the same transformer layer with synergistic correlation. I find this to be a very inspiring design. Moreover, the design of knowledge collaboration is closely integrated with this unified paradigm.\n\n(3) The experiments are very solid and thorough, clearly demonstrating that UniKE effectively addresses multimodal knowledge editing tasks under various setups. Meanwhile, the authors have also provided the implementation code for the experiments.\n\n(4) I commend the authors for conducting an extensive set of ablations and analyses, which are very helpful in understanding the impact of each component within UniKE.\n\n(5) Additionally, I believe that the method proposed by the authors is not only applicable to knowledge editing tasks. By converting in-context learning into the representation space and avoiding the need to increase the context window space, it better synergizes with parameter update learning. I consider this to have significant implications for further studies on how to construct more powerful MLLMs.\n\nWEAKNESSES:\n(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).\n(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. \n[1] Zheng, Ce, et al. \"Can we edit factual knowledge by in-context learning?.\" arXiv preprint arXiv:2305.12740 (2023).\n[2] Dong, Qingxiu, et al. \"Calibrating factual knowledge in pretrained language models.\" arXiv preprint arXiv:2210.03329 (2022).\n[3] Meng, Kevin, et al. \"Locating and editing factual associations in GPT.\" Advances in Neural Information Processing Systems 35 (2022): 17359-17372.\n\nQUESTIONS:\nSee the weaknesses.\n\nLIMITATIONS:\nThe authors have adequately discussed the limitations and potential negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 9, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nUniKE is a unified framework for multi-modal knowledge editing that includes three main aspects:\n1.Knowledge Separation: UniKE divides knowledge into factuality and semantic spaces to manage and coordinate different types of knowledge more effectively.\n2.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.\n3.Multi-step Editing: UniKE supports single-step, multi-step sequence, and cross-task editing while maintaining high reliability, generality, and locality.\nThrough these innovations, UniKE significantly improves performance in multi-modal knowledge editing tasks.\n\nSTRENGTHS:\n1.The paper introduces UniKE, a novel framework that seamlessly integrates intrinsic and external knowledge editing, enhancing the model’s ability to handle complex multimodal information effectively. The motivation behind the work is clear, and the article is well structured, guiding the reader through the innovative approach and its benefits.\n\n2.By disentangling knowledge into semantic and truthfulness spaces, the proposed method ensures robust collaboration between different types of knowledge, significantly improving the model's reliability, generality, and locality. The method's effectiveness is demonstrated through comprehensive experiments across various settings, consistently outperforming existing state-of-the-art methods.\n\n3.UniKE's design allows for application across different multimodal models and editing scenarios, making it a versatile and robust solution for enhancing multimodal language models.\n\nWEAKNESSES:\n1.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.\n\n2.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.\n\n3.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.\n\n4.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.\n\nQUESTIONS:\nPlease address the comments in the weaknesses section.\n\nLIMITATIONS:\nThe limitations of the article have been discussed by the authors", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-07"}

comments: 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.

STRENGTHS:
1. The paper is well-written and easy to follow and the motivation is clear and reasonable.
2. The paper gives a unified perspective on the intrinsic refinement of knowledge and the strategic reorganization of external knowledge, which can enhance subsequent research endeavors.
3. The experimental settings (one-step editing, sequential editing, cross-task editing) are fair.

WEAKNESSES:
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:
Please refer to the weakness below.

LIMITATIONS:
The paper briefly mentioned limitations.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-13