Can We Edit Multimodal Large Language Models?
Authors: Siyuan Cheng
Bozhong Tian
Qingbin Liu
Xi Chen
Yongheng Wang
Huajun Chen
Ningyu Zhang
Venue: EMNLP 2023 Main (2023)
Keywords: Model Editing
Multimodal Language Models
Large Language Models
TL;DR: We construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs.

Abstract: In this paper, we focus on editing multimodal Large Language Models (LLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a higher level of scrutiny and careful consideration in the editing process. To facilitate research in this area, we construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs and establishing a suite of innovative metrics for evaluation. We conduct comprehensive experiments involving various model editing baselines and analyze the impact of editing different components for multimodal LLMs. Empirically, we notice that previous baselines can implement editing multimodal LLMs to some extent, but the effect is still barely satisfactory, indicating the potential difficulty of this task. We hope that our work can provide the NLP community with insights.

Can We Edit Multimodal Large Language Models?
Siyuan Cheng** v Bozhong Tian*;; Qingbin Liu Xi Chen ?f Yongheng Wang", Huajun Chen Ningyu Zhang*
Zhejiaang University Zhejiang University Ant Group Joint Laboratory of Knowledge Graph
Donghai Laboratory Platform and Content Group, Tencent Zhejiang Laboratory {sycheng, tbozhong huajunsir, zhangningyuj@zju. edu. cn
fqingbinliu, jasonxchen) com, wangyh@zhejianglln. com
Abstract
A ladder the ground.
street barrier
ground.
In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared editing single-modal LLMs, multimodal model editing is more challenging, which de- mands a higher level of scrutiny and careful consideration in the editing process. To fa- cilitate research in this area, we construct new benchmark, dubbed MMEdit, editing multimodal LLMs and establishing suite of innovative metrics for evaluation. We conduct comprehensive experiments involving various model editing baselines and analyze the impact editing different components for multimodal LLMs. Empirically, notice that previous baselines can implement editing multimodal LLMs to some extent, but the effect still barely satisfactory, indicating the potential dif- ficulty of this task. We hope that our work can provide the NLP community with insights
Image
Edit
understanding emrrr
Before Editing
After Editing
Retains
This pirture
Updates
dejicts
clock looks like little bear.
street barrier,
Figure 1: Overview of the multimodal model editing
task. The editing target is update the model's under- standing of the edited input (e.g., image or text), while ensuring its interpretation unrelated inputs remains consistent as possible.
may stem not just from LLMs, analogous to human errors like misreading or misrecognition (e.g.. color blindness affecting color identification in images). As shown in Figure 1, before the editing, the model misidentified the object as a "ladder" instead of the correct "barrier", resulting in an erroneous pre- diction. After the editing, the model accurately recognized the "barrier". Note that the utility of multimodal LLMs (Yin et al., 2023) is increas- ing, yet there is lack of corresponding dataset resources and benchmarks for editing multimodal large language models.
1 Introduction
deployy
With the widespread deployment of Large Language Models (LLMs) (Zhao et al., 2023), the necessity maintain their knowledge accurate and current without incurring significant retraining costs is becoming increasingly paramount (Sinitsin al., 2020). Previous research has introduced knowledge editing methodologies designed to incrementally infuse a language model with a new set of facts (Mitchell et al., 2022a; Han et al., 2023; Hartvigsen et al., 2022; Zhong et al., 2023; Gandikota al.. 2023; Yao et al., 2023).
To facilitate research in this area, we take the first step to construct Multimodal Model Editing benchmark: dubbed MMEdit, which encompass two sub-tasks: Editing VQA and Editing Image Captioning. Specifically, we follow single-modal model editing approaches (Mitchell et al., 2022a; Cao et al., 2021: Mitchell et al., 2022b) to construct the datasets, which extends the previous evalua- tion principle, namely Reliability?, 2 and Generality to multimodal settings. For Reliability evaluation, start with rigorous data collection, gathering underperforming multi-
Different from single-modal model editing, the task of editing multimodal LLMs presents consid- erable challenges, given their inherent diversity and complexity. Specifically, incorrect outputs from multimodal models may stem from the synergistic effects of various modalities. Incorrect outputs
"Equal contribution.
"Corresponding author.
2The metric used to measure the success of editing target. "It measures whether unrelated facts retain their outputs. "It measures the success of editing related knowledge.
1Code and dataset are available in https://githuu..... 2j EEasy/.i..

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modal model data create a dedicated reliability editing dataset ($3.2.1). For Locality evaluation, we split into the textual and multimodal locality to evaluate the stability of multimodal LLMs ($3.2.2). For Generality evaluation, similar to Locality, we divide it into textual and multimodal generality and utilize ChatGLM (Du et al.. 2022), and Sta- ble Diffusion (Rombach et al., 2022) to generate rephrased text well rephrased images for eval- uation ($3.2.3). We evaluate several knowledge editing approaches on MMEdit. Empirically, we notice that current editing approaches are effective for editing the textual model in the multimodal language model but not as effective for editing the vision module. For example, in editing the lan- guage module of the BLIP-2 model, the reliability of MEND can reach 92.6%, but only attain 14.1% % if editing the vision module, indicating the potential difficulty and opportunities of this task. In general, our primary contributions are as follows:
solution by applying the encoder of Transformers to images. CLIP (Radford et al., 2021) proposes a method, which uses multimodal pre-training to convert classification as a retrieval task that en- ables the pre-trained models to tackle zero-shot recognition. Recently, the advancement of LLMs, such as LLaMA (Touvron et al., 2023), BLOOM (Scao et al., 2022), and ChatGPT (OpenAI, 2022), has been bolstered by scaled-up training data and increased parameters, yielding significant recent success. These models showcase impressive lan- guage understanding, generation, and knowledge reasoning capabilities, enhancing their ability to comprehend natural language and generate high- quality, context-based text. The evolution of large language models has spurred the widespread use of auto-regressive language models decoders in vision-language tasks. Utilizing cross-modal transfer, this approach enables knowledge sharing between language and multimodal domains (Gao et al., 2023; Liu et al., 2023; Li et al., 2023a; Ye et al., 2023; Zhu et al., 2023; Li et al., 2023b; Zhang et al., 2023).
We take the first step to investigate editing mul- timodal LLMs, which extends model editing to multimodal settings.
We propose MMEdit, a new benchmark, to evaluate the reliability, locality, and generality of multimodal model editing approaches.
2.2 Model Editing
LLMs (Zhao et al., 2023) primarily derive knowl- edge from the training corpus. Yet, the quality of the dataset is not always guaranteed, potentially integrating harmful incorrect information into the model (Hernandez et al., 2023). One solution is retraining models with updated knowledge, though this might be unfordable and difficult to implement. Alternatively, tieettnnig with a few updated facts could be considered, but it risks over-fitting and catastrophic forgetting (Zhai et al., 2023). To ad- dress these issues, (Sinitsin et al., 2020) proposes Model Editing, which aims efficiently and ac- curately alter the factual knowledge stored within models. This approach is applied in various do- mains (Mao et al., 2023; Onoe et al., 2023; Xu al., 2022b; Wang et al., 2023a; Li et al., 2023c; Cheng et al., 2023), with an increasing number of studies investigating the impact of editing (Ilharco et al., 2023; Gupta et al., 2023; Hase et al., 2023a; Cohen et al., 2023; Wu et al., 2023; Wang al., 2023b; Gandikota et al., 2023; Li et al., 2023d; Hase al., 2023b). Presently, there are three primary types of model editing approaches: 1) Meta-learning Method, 2) Locate-Then Eedit Method, and 3) In- Context Knowledge Editing Method.
We conduct experiments with various base- lines, demonstrating that while current method- ologies can somewhat aid in multimodal edit- ing, the outcomes still fall short of complete satisfaction. We will make the code and datasets publicly available for future research purposes.
2
Related Work
2.1 Multimodal Language Models
Multimodal Learning (MML) (Xu et al., 2022a; Yin et al., 2023) provides holistic upproach to crafting Al models that can extract and correlate information from various data modalities. Due to its societal significance, MML has established a foothold in the research community, solidify- ing itself as a crucial field of study over the past decade. Vision-languaae pre-training is one of the important branches of MML, which aims to learn multimodal foundation models with improved performance on various vision and language tasks. Vision Transformer (ViT) (Dosovitskiy et al., 2021) is a seminal work that contributes an end-to-end

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Before Editing
Large Language Model (LLM)
Image Encodar
Q-former
bird flying the sky.
After Editing
What are shown in the photo?
A kite flying in the sky.
Multimadal Language Model
Querles
Tool
in on the sky
A. kirk name Heodee, Bathhme semaa wrrggs and cags A like lightvcialt object thanis thom
Vision Area
Language Area
on the sky
Vision Edit
Language Edit
Utilizing multimodal LLM (e.g.. BLIP-2 OPT) an example, we dissect the comprehensive multimodal LLM into two components (Vision module and Textual module). The model's erroncous output could potentially stem from either both of these modules. Drawing analogy with human errors in "vision" and "speech", we apply model editing methods to these two components, thereby changing the model to refine its output.
Figure 2:
Meta-learning Method. MEND (Mitchell et al., 2022a) and Knowledge Editor (KE) (Cao al., 2021) propose approaches involving an external editor, capable of learning the optimal parameter set, 0, for knowledge updating, while concurrently imposing constraints to maintain model stability, CaliNET (Dong et al., 2022) and T-Patcher (Huang et al., 2023), drawing inspiration from (Dai et al., 2022), introduce additional trainable parameters into the feed-forward module of Pretrained Lan- guage Models. SERAC (Mitchell al., 2022b) utilize an explicit memory to store edits and learns to reason over them to modulate the base model's predictions as needed.
obtained from demonstrations directly concatenated within the input context. A novel editing paradigm has recently emerged that capitalizes on the abil- ity LLMs to comprehend context (Zheng et al., 2023), thereby enabling the performance of context- based model editing, guiding the model's genera- tion process, and offering an efficient, lightweight approach to model editing.
Model editing methods date largely cater single-modal scenarios, leaving gap in multimodal editing. To the best of our knowledge, we are the first to investigate multimodal model editing for LLMs and provide a new benchmark to facilitate research in this area.
Docate-Then-Fdite Method. ROME (Meng et al., 2022a) proposes approaches that employ causal mediation analysis to identify the area for editing. ROME discovers that memorized factual associa- tions can be pinpointed to a specific location within a GPT model. However, a notable limitation of ROME its ability only to edit one fact at a time. To address this, Meng et al. (2022b) proposes a new method known as MEMIT, which is a successor to the previous work ROME, which performs rank- modification of the MLP weights of single layer write memory into the model directly.
Editing Multimodal LLMs
We illustrate the proposed task of multimodal edit- ing in Figure 2. We will introduce the task definition ($3.1), 1), dataset construction details in ($3.2), the multimodal models ($3.3), and the baselines ($3.4) we used in the experiments.
3.1 Task Definition
Assuming we have a multimodal LLM ff parameter- ized by (consisting of two parts, fvision and fiex parameterized by @vision and 6rext) that map the input ig and x₂ to the prediction to you where ie refer to the editing image input, Xe refer to the editing text prompt input and yo denote as the origin output. We denote M as a symbolic representation for a par-
In- In-Context Knowledge Editing Method. In- Context Learning (ICL) (Brown et al., 2020) signi- fies training- free paradigm where knowledge is

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TASK
Train
Test
I.-Locality
M-Locality
E-VQA
6,346
2,093
4,289
5,046
E-IC
2,849
1,000
4,289
5,046
How many skyscropers there!
Table 1: The statistic of datasets for the E-VQA and E-IC sub-tasks. L-Locality and M-Locality are the test sets for knowledge locality evaluate the rest of the knowledge in multimodal models when successfully updating specific facts.
What the number of high-rise buildinngs city?
When did prison break
out?
The vision encoder serves a critical function in the multimodal language model, transforming images into vector representations for co-encoding alongside natural language text. Consequently, we must take into account the potential ramifications of any modifications to this module. We construct the dataset denoted Dioc-v for test Mingg and calculate delineated below:
Edit scope
target
In-soope
ou-cofe
Figure 3: Taking the text modality as an example, Edit target and its generalization pertain to in-scope, which involves querying the quantity of skyscrapers in given image, while the out-of-scope refers to inquiries about the publication date. In-scope inputs require editing, whereas out-of-scope inputs remain unchanged.
M Img loc
ticular metric, with subscripts indicating specific metrics and superscripts representing variations in edit data. We prepare the editing datasets stated in 13.2.1, which present Dedit- Inspired by Yao al. (2023), we introduce series of multimodal model editing metrics.
B((ivio.)))-DDe (linsi))))== ((xxx)))))))) (3) where (1v, yv) is the out-of-scope data, and Be denote the parameter updated by edit data (ee.
Generality. Throughout the editing process, it is not adequate merely amend individual erro- neous inputs. The revised model should also retain the capacity for generalization and consistently produce congruent outputs for equivalent inputs (e.g., rephrased sentences), as shown Figure 3. While previous unimodal model editing tasks only required consideration of the rephrased text, multimodal scenarios necessitate the generaliza- tion of images well. To address this, we in- troduce two generalization considerations: gen (T-Generality) and Mgen (M-Generality), which expressed follows:
Reliability, Editing reliabiiity is needed to change prediction from Yo to Ye- Intuitively, what we need is an updated de with fiee. 0e) To measure the reliability, we use the editing accuracy, described by the following:
Myel = Bita [L (L
(1)
where de refers to the edited parameters.
Locality. To maintain the model's stability, min- imizing the unintended side effects of editing the model's broader knowledge base is imperative. pursuit of this objective, we introduce two met-
rics: M (T-Locality) and ! (M-Locality), both of which are designed to preserve the model's stability during the editing process. Given that the knowledge in the multimodal language model is in- herited from LLMs, safeguarding this knowledge is paramount. With this aim in mind, we set aside the model's 's visual discrimination module and instead employ rudimentary question-nnd-answee datasets Dise-t as we stated in $3.2.2. We define the question as x and the answer as y. as below:
M = -B(x)--(xx)) [1 (4)
M ((ii)) [1 (e
(5)
where i, presents the rephrased image, x, refers to the rephrased text prompt, and Nxx denotes in-scope objects of
3.2 Datasets
The dataset MMEdit we constructed mainly con- tains two subtasks: Editing VQA (E-VQA) and Editing Image Captioning (E-IC).
MTTxt B (eeeeee))-Oe Delit
(ry0--pest
(2)

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Diverse Text Generation
Textual Locality Dataset. To evaluate the sta- bility of the language model, we leverage the NQ dataset (Kwiatkowski et al., 2019), previously used in MEND, as a benchmark for the stability of the LLM component within the model. We specifically use the model's 'ss output pre and post-editing to con- struct KL scatter plot, facilitating constraints on the model's edits. Additionally, we calculate the proportion of instances maintaining top-1 status, further quantifying the model's stability.
Question:
"What you looking for?"
Prompt Creation:
"Please rowrite the following question in a different way. Please do include the original question... What are you looking for?"
Prompt Optimization
ChatGLM:
What is it that you seck?"
Diverse Image Generation
MultiModal Locality Dataset. Similarly, it's cru- cial to verify the impact of editing on the visual module. Hence, we utilize a straightforward dataset OK-VQA (Marino et al.. 2019) in the realm multimodality, serving as measure of the locality for the multimodal visual module. Once again, we update the KL dispersion constraint using logits both before and after the editing process.
Caption:
Stable Diffusion:
"A wood table holding an assortment cooking utensils."
Prompt Creation:
"A wood table holding an... Positive prompt: masterpioce, best quality... Negative prompt: EasyNegative, distigured.."
Prompe Optimization
Figure 4: Generality dataset construction process.
3.2.3 Generality Dataset Construction
We propose two forms of generality within mul- timodal model. The overall process of generality dataset construction is shown in Figure 4.
3.2.1 Reliability Dataset Construction
To benchmark our experiments, we selected two common multimodal tasks: Visual Question An- swering (VQA) (Antol et al., 2015) and Image Captioning (Herdade et al., 2019). VQA is to de- vise algorithms that can not only comprehend the visual content within an image, but also understand the natural language used to inquire about that image, and subsequently generate precise answers those queries. Image Captioning is devise algorithms capable of comprehending the visual content of an image, subsequently generating a coherent and precise description of the image in natural language. In this study, we opt for BLIP-2 OPT. Our foundational edit data originates from suboptimal entries across two eval datasets, namely, VQAv2 (Goyal et al., 2017) and COCO Caption (Chen et al., 2015).
Textual Generality Dataset. To be noted, LLMs exhibit robust conversational and power- full problem-solving capabilities, which enables us to formulate task instructions, whereby we can instruct the model to produce analogous text inputs. For the E-VQA task, we utilize ChatGLM (Du al., 2022: Zeng et al., 2022) to generate similar queries. However, for the E-IC task, due to the succinctness and relative straightffrwarddess of the prompts, the quality of the model's generated output is not satis- factory. Therefore, employ manually written template with 20 prompts to replace the original ones randomly.
Visual Generality Dataset. The diffusion model (Ho al., 2020) has garnered significant success in the realm of image generation in recent years. Sur- passing the original state-of-the-an model: Genera- tive Adversarial Networks (GAN) models (Goodfel- low et al., 2014). The diffusion model has excelled in numerous commendable performance across various applica- tion domains. Stable Diffusion (Rombach et al., 2022) is a latent text-to-image diffusion model pable of generating photo-realistic images given text input. We utilize Stable Diffusion 2.1 for gen- erating reinterpreted images. This dataset, drawing upon caption descriptions from the COCO dataset,
Besides the foundational edit data, utilizing ad- ditional data is crucial. This data not only aids the editing process but also validates the efficacy of the changes, assessing model edits for both stability and generality.
tasks and has shown
3.2.2 Locality Dataset Construction
We must deliberate on the effects of editing on the language function within a multimodal model, analogous to how we evaluate various cognitive regions of an individual's ' brain post-surgery.

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is leveraged to evaluate the model's capability for image generalization.
utilizes hyper network bidirectional-LSTMM) with constrained optimization, which used predict the weight update during inference.
3.3
Multimodal Language Models
SERAC. SERAC (Mitchell et al., 2022b) intro- duces a memory- based model editing approach, which leverages an explicit memory system to cache edits. This memory is subsequently used to adjust the output of the base model during inference. The system utilizes small auxiliary scope classifier alongside cownterfactual model. The role of the scope classifier is to ascertain whether the input is within the ambit of the memory cache. Should the input be found within this scope, it is combined with the most relevant cache item and input into the counterfactual model for prediction.
BLIP-2 OPT. BLIP-2 (Li et al., 2023b) is generic and efficient pre-training strategy that boot- straps aision-langure pre-training from off-the- shelf frozen pre-trained image encoders and frozen large language models. The model utilizes lightweight Quering Transformer to bridge the gap between vision modality and text modality and achieves state-of-the-aa performance on various vision-language tasks. select the BLIP-2 OPT as our basic edit model, which utilizes the ViT-L in the vision block, and select the unsupervised-traing, OPT model for decoder-based LLM.
In-Context Knowledge Editing. In-Context Knowledge Editing (IKE) (Zheng et al., 2023) constructs k demonstrations C == (cI...., cx), fol- lowing the approach outlined in Liu al. (2022). This method employs an unsupervised retriever based on cosine similarity to fetch demonstra- tions from the training set prior to injecting fact f = (x",y" into Language Models. The x² is the prompt to probe the factual knowledge in mod- els (e.g.. The president of the US is), and y" will be the editing target Joe Biden. The ranking of in-context demonstrations also hinges on cosine similarity: cos(c1, f) < cos(c2, f) < < cos(ca. f). where Cı...., Cg are sequen- tially arranged in the context from left to right. Demonstrations C can be viewed as an externally augmented knowledge base, primarily designed to guide the generation within LMs. Its ultimate objective is to maximize P(y x, C) when the prompt falls within the editing scope of the target prompt x".
(Zhu et al.. 2023) is a potent vision-language model akin to BLIP-2, lever- aging frozen visual encoder in tandem with the frozen Vicuna (Chiang al., 2023). Vicuna, built upon LLaMA, is reported achieve 90% of Chat- GPT's performance based on GPT-4's evaluation criteria. MiniGPT-4 adds single projection layer to align the encoded visual features with the Vi- cuna language model. And MiniGPT-4 employs the same pre-trained vision component of BLIP-2 that consists of a Vit-G/14 from EVA-CLIP (Sun et al., 2023) and Q-Former.
MiniGPT-4. MiniGPT-4
3.4 Baselines
Finetune. Fine-tuning has emerged as a widely strategy for adapting pre-trained language models to specific tasks or domains (Cortes et al., 2015). In our exploration, we delve into two distinct fine-tuning methodologies: one focusing on the last layer of the language model. Take the BLIP-2 OPT model decoder layer of OPT model. The other targets the vision block within the multimodal language model, specifically, we finetune the Q-former model to overfit the editing dataset.
straeyed
example, we finetune the st
4
Experiments
4.1 Results
In this part, we present a comparative analysis of multiple editing methods on MMEdit. The results of these comparisons are displayed in Table 2. Af- ter this, we delve into tripartite evaluation of the experimental results, including three aspects of Re- liability, Locality, and Generality. Furthermore, analyze Locality and Generality through text and visual modalities and provide several editing cases in Figure
MEND. Model Editor Networks with Gradient Decomposition (Mitchell al., 2022a) conducts efficient local edits to language models with single input-output pair. Essentially, MEND learns to transform the gradient of fine-tuned LLMs, which utilizes a low-rank decompasition of gradients.
Knowledge Editor. KE (Cao et al., 2021) a method that can edit wrong knowledge in language models without re- re-training the whole model. KE
Reliability. From the results, all model editing methods outperform the base methods in Reliabil-

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EDrrING VQA
EarriGG IMAGE CAPTION
Method
Relibility
T-Cenerlliy
T-Locality
M-Lacalit
Relishiiiy
T-Ginerality
1 T-Lacaliyy T M-Lacclity
BLIP-2 OPT
Size: 3.88
Model
0.80
0.00
100.0
100.0
0.00
100.0
Base Methods
FT (Vision block)
56.28
29.88
100.0
11.32
0,08
0.00
100.0
7.31
layer)
Koowledge Edltor
67.80
63.00
97.32
45.89
69.00
62.80
96.21
45.55
Model Editing
In Contest Editing
99.95
91.59
13.16
1,88
96.70
78.20
13.36
2.17
SERAC
91,20
91.40
100.0
0.33
94.40
96.00
100.0
0.47
MEND
92.60
90.50
96.07
65.15
65.00
38.00
92.67
53.72
MiniGPTT44
Sive: 7.30
100.0
Base Methods
FT (vision black)
39.58
0.98
100.0
3,96
0.63
0.00
100.0
5.13
PT (last layer)
39.57
0.58
72.01
16.42
2.75
0.00
35.52
9:28
Knowlodge Editor
87.77
86.62
97.15
55.77
24.20
96.78
Model Editing
In-Coneett Editing
71.72
59,80
12.51
$7.20
MEND
Table 2: Main results the MMEdit. T-Locality, M-Locality refer to the textual and multimodal stability. T-Generality represents textual generality. Reliability denotes the accuracy of successful editing.
ity. Particularly, IKE and SERAC, methodologies leveraging external memory for editing, exhibit commendable performance in multimodal language models. We observe that the fine-tuning method demonstrates poorer performance than the model editing method. Note that merely fine-tuning the parameters of the LLM or the modal fusion block does not adequately capture the characteristics of the multimodal data. We analyze the reasons as
Locality. Several traditional editing methods main applicable in multimodal editing, proving valuable for effectively modifying the knowledge within the model and rectifying its outputs. How- ever, IKE and SERAC, despite their superior perfor- mance in Reliability, exhibit poor performance on the M-Locality due to their lack of constraints on it, indicating that although these external memory- based editing techniques undoubtedly succeed in fixing the outputs, their efficacy in stabilizing inter- nal knowledge within the models leaves room for improvement. As for T-Locality, the majority of Model Editing methods obtain good performance, with IKE once again falling short. The underlying reason is that the other three approaches impose constraints on T-Locality, whereas IKE, as an In- Context Learning method, lacks robust constraint mechanism, resulting in subpar performance.
M-Locality
KIA
T-Locality
Generrallly
T-Generality
MEND
MEND
SERAC
SERAC
IKE-
KE
KE
0.0 0.2 0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Figure 5: Generality of different editing methods.
Generality. We undertake a comparative explo- ration of various methods' text and image gener- alization capabilities with MiniGPT-4 in E-VQA. Note that KE tends to display a lesser degree of image generalization, predominatlly due its inher- ent consideration of M-Locality during the training phase. Consequently, the image generalization effi- ciency of meta-learning methods tends to fall short when compared to memory-basee methods. On the other hand, the superior image generalization capability exhibited by memory-based methods is achieved the cost of compromising M-Locality, resulting in significantly lower levels of M-Locality. Through our evaluation of diverse editing methods,
follows: the data used for fine-tuning differs sig- nificantly from the original model, such as the Q-former and OPT model, which need to collab- orate effectively. Simply fine-tuning one of these modules may not capture the spec-fi charac- teristics accurately. On the other hand, fine-tuning all modules incurs a significant resource overhead. Moreover, based on our experimental results, observe that fine-tuning can lead substantial changes in the original model, often resulting in the loss of other knowledge, particularly evident in multimodal datasets.

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Editiig
Editing
Editting
What the man doing?
What are shown the photo?
What is the train number?
A photo getting on bus that has bicycles the rack.
Roarding
17788.
After Ediring
After Editing
After Editing
What is man doing?
What are shown in the photo?
What is the train number?
Skatebourding
A person getting on a bus that has bicycles on the rack.
18688.
successful VQA editing SERAC)
successful Image Caption editing SERAC)
VQA editing (By IKE)
Figure 6: Cases of multimodal model editing. Top: The output before editing. Bottom:
The output after editing.
1.0-
Edit Text
22
Edit Text
edit more modules and analyze their impact on vi- sual and textual knowledge. The results shown in Figure 7. For the BLIP-2 OPT model, we inves- tigate the distinctions in editing the Q-former and OPT on the VQA dataset. Regarding the MiniGPT- model, we mainly focus on the distinctions editing the last few layers of the Ilama Ilama_proj and Vicuna models. The selected editing approaches for analysis are MEND, KE, and FT, which enable us to specify the editing area.
0.8
Vision
0.8
Edit Vision
Reliability
T-Genwwaliig
0.6
0.6
0.4
0.0
0.0
MEND
Editiing Methods
Editing Methods
1.0
Z22 Edit Text
1.0
Ez Edit Text
0.8
Edit Vision
Vision
M-Generaity
M-Locality
0.8
0.6
The results highlight that editing the vision mod- ule is more challenging than editing the language module (also see the failure editing in Figure 6). We argue that this difficulty may be attributed to the model's architecture. Editing the last layer of the LLM allows for direct modification of the output, while modifying the vision module only affects the input to the LLM, resulting in relatively less impact on the model. Concretely, various modal- ities reside in distinct spaces, which implies that the factual knowledge may be stored in separate parameters within the model. Considering that the LLMs possess large number of parameters, this aspect becomes even more critical for multimodal models. Thus editing the language model can lead to significant performance improvements. Notably, the visual module in the model plays a crucial role in image comprehension, thereby suggesting that
0.4
0.4
0.2
0.2
0.0
MEND
KE
FT
MEND
FT Editing Methods
KE
Editing Methods
Figure 7: Results of editing different components,
we recurrently identify that image generalization performance tends to be less robust than text gener- alization.
4.2 Editing Different Component
We further analyze the variations in editing different regions of the multimodal model. contrast to editing single-modal models, due to the complexity and diversity multimodal models, we can try to

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future work needs consider information from different modalities simultaneously.
Vision, ICCV 2015, Santiago. Chile, December 7-13, 2015, pages 3425-2433. LEEE Computer Society.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neclakantan, Pranav Shyam. Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, 1lya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems 33: Anmual Conference on Neural Information Pro- cessing Systems 2020, NeurlPS 2020, December 6-12, 2020, virtual.
5
Conclusion
In this paper, we introduce multimodal model edit- ing, with new benchmark MMEddit. Empirically, we analyze the effectiveness of various model edit- ing baselines and explore their impact on different components (e.g., visual and text).
Acknowledgment
We would like to express gratitude to the anony- mous reviewers for their kind comments. This work was supported by the National Natural Sci- ence Foundation of China (No.62206246), Zhe- jiang Provincial Natural Science Foundation of China (No. LGG22F03001 1), Ningbo Natural Science Foundation (2021J190), Yongjiang Talent Introduction Programme 3021A-156-CB Zhejiang Provincial Science and Technology Plan Project (2023C01 120), CCI-Tencent Rhino-Bird Open Re- search Fund, and Information Technology Center and State Key Lab of CAD&CG, Zhejiang Univer- sity.
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021. Editing factual knowledge in language models. EMNLP.
Xinlei Chen, Hao Fang, Tsung- Yi Lin, Ramakr- ishna Vedantam, Saurabh Gupta, Piotr Dollár, and C. Lawrence Zitnick. 2015. Microsoft COCO cap- tions: Data collection and evaluation server. CoRR, abs/1504.0033
Siyuan Cheng. Ningyu Zhang, Bozhong Tian, Xi Chen, Qingbing Liu, and Huajun Chen. 2023. Editing language model-based knowledge graph emheddings.
6 Limitations
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng. Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An open- source chathot impressing gpt-4 with 90%* chatgpt quality.
Models. We only edit several basic multimodal LLMs, leaving many others behind. Besides, due to the resource limitation, the number of parameters for the multimodal LLMs we edit below 10B, and we cannot afford to edit LLMs with a larger number parameters such as the 65B LLaMA Adapter V2 (Gao et al., 2023).
Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2023. Evaluating the ripple effects of knowledge editing in language models. CoRR, abs/2307. 12976.
Efficient Vision Editing. In this paper, our anal- ysis has been primarily focused comparing the varied effects of existing editing methods across modules of different modalities. However, the re- sults are not satisfactory. Moving forward, our primary objective is to explore how to efficiently and accurately edit information across other modal- ities. This includes investigating techniques such as co-editing between different modalities by pinpoint- ing the knowledge within the multimodal model and identifying the content requiring modification.
Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, editors. 2015. Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Pro- cessing Systems 2015, December 7-12, 2015, Mon- treal, Quebec. Canada.
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022. Knowledge neurons in pretrained transformers. In ACI..
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022. Calibrating factual knowledge in pretrained language models. In EMNLP. Findings EMNLP.
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and Fei Huang, 2023. mplug-owl: Modularization empowers large language models with multimodality. CoRR, abs/2304. 14178.
Hyper- Paper-Paramekers
Maxlter
Edit Num
Optimizer
D D VQA
20000
ASGD
le-5
20000
ASGD
le-5
BLIP2
Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen. 2023. A sur- on multimodal large language models. CoRR, abs/2306. 13549.
D DE-QQA
MiniGPT-4
20000
ASGD
le-5
D-IC
MiniGPr-4
20000
ASGD
le-5
Table 4: FT-last-layer hyper-parameters
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang. Yifan Xu, Wendi Zheng, Xiao Xia, et al. 2022. Glm-130b: An open bilingual pre pre-trained model. arXiv preprint arXiv:2210.02414.
this section, we describe the implementation of our experiments detail, including the training procedures, backbone model, and hyperparameters for each dataset.
Yuexiang Zhai, Shengbang Tong. Xiao Li, Mu Cai, Qing Qu Yong Jae Lee, and Yi Ma. 2023. Investigating the catastrophic forgetting in multimodal large language models. CoRR, abs/2309. 10313.
Hyper Parameters
Maxlter
Edit Num
Optimizer
LR
D VOA BLIP2
20,000
Adam
le-5
DE-IC BLIP2
20,000
Adam
le-5
Ao Zhang, Hao Fei, Yuan Yao, Wei Ji, Li, Zhiyuan Liu, and Tat-Seng Chua. 2023. Transfer visual prompt generator across llms. CoRR, abs/2305.01278.
D voa
25,000
AdamW
5e-4
MinGGPT-4
DE DC
MiMGGPT-1
35,000
AdamW
Sc-4
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang. Xiaolei Wang. Yupeng Hou, Yingqian Min, Be- ichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang. Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023. A survey of large language models. CoRR. abs/2303.1820 18223.
Table 5: KE haper-aarraees
SoyperParramtter
Maxiter
Edit Num
Optimizer
T.R
DE VQA
15,000
Adam
le-5
BLIP2
DE-IC
15,000
Adam
le-5
D V--VOA
20,000
Adam
le.5
MinGPT-4
Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023. Can we edit factual knowledge by in-context learning? CoRR. hls/2305.127400
D DE-IC
MiniGPT-4
30,000
Adam
le-5
Table 6: SERAC parer-parameters
Zexuan Zhong, Zhengxuan Wu, Christopher Man- ning Christopher Potts, and Danqi Chen. 2023. Mquake: Assessing knowledge editing in lan- guage models via multi-hop questions. CoRR, 1bb/2305.14795.
Hyper-Parameters Parameters
Maxiter
Edit Num
Optimizer
LR
R-VQA
BLIP2
20,000
1
Adam
le-6
DE-IC BLIP2
20,000
1
Adam
lc-6
DE-VQA
20,000
Adam
le-6
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023. Minigpt-4: Enhancing vision-lange lisionelanguage understanding with advanced large language models. CoRR, abs/2304. 10599.
MinIGF-11
D
MInIGPT-4
20,000
Adam
le-6
Table MEND hyper-parameters
A Appendix
Our code is available in the supplementary ma- terials for reproducibility.
Hyper-Parameters
MaxIter
Edit Num
Optimizer
LR
DE- E-VQA
BLIP2
40000
ASGD
le-5
DC
40000
1
ASGD
le-5
BLIP2
E-VQA
MOuGPT-4
40000
1
ASGD
le-5
DE-IC
Miniaer
40000
ASGD
Table 3: FT-vision peper-parameters


Reviews:
[{"review_id": "Sl33Sxx9gj", "reviewer": "Reviewer_sCgZ", "summary": "", "questions": "Question A) If I understand the metric equations correctly, the expectations are over cases where you use a single edit example to update the model; if this is the case, is there a way to test a technique's performance when a larger set of edits are made? I could imagine the different techniques having different behaviors as the number of edits increases.\n\nQuestion B) In line 264, what does \"in-scope objects of x\" mean? Specifically, what are the objects, and how do you determine which are in-scope?\n\nQuestion C) For measuring Locality, is there any guarantee that the examples in D_loc-t and D_loc-v are truly unrelated to the edit examples? For example, if you edit the model based on a VQA example from one dataset, if there are similar examples in the other VQA dataset used as D_loc-v then seeing a change there might actually be desirable.\n\nQuestion D) In the paragraph starting on line 288, what is the \"supplementary data\"? It seems like the description of it is missing; if it is present then this paragraph should be moved or re-written to make it clear what is being referred to.\n\nQuestion E) Why are the results split between Table 2 and Figure 5? I would prefer having a complete view of all main results in a single table or figure.\n\nQuestion F) Is the discussion of SERAC starting at line 483 correct? The model's image generalization seems fine, and I don't think it has \"inherent consideration of M-Locality during the training phase\" because it has terrible performance in M-Locality. Is this just a typo and you meant some system other than SERAC, like KE?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for reviewing our paper and the valuable feedback. In addition to the general updates, we address your concerns here.\n> Question A) If I understand the metric equations correctly, the expectations are over cases where you use a single edit example to update the model; if this is the case, is there a way to test a technique's performance when a larger set of edits are made? I could imagine the different techniques having different behaviors as the number of edits increases.\n\nExperiments exploring various numbers of edits are crucial in model editing to understand the impact of edit quantity. We omitted this experiment from our paper for two reasons: First, previous research in model editing (MEMIT[1], Survey[2]) has indicated a diminishing effect as the number of edits increases. Secondly, our limited computational resources prevented us from executing the n-edits experiment. We revisit the n-edits experiment yet regret our inability to compare against all baseline methods due to constraints in time and resources. We evaluated MEND's efficacy in editing the MiniGPT-4 model on E-VQA using varying edit counts, specifically {1,2,4,8}, with the results detailed below:\n| Edit Num | Reliability | T-Generality | T-Locality | M-Locality |\n| -------- | -------- | ----------- | --------- | -------- |\n| 1 | 95.51 | 95.27 | 98.73 | 71.33 |\n| 2 | 94.40 | 94.80 | 97.83 | 61.18 |\n| 4 | 93.30 | 93.75 | 97.50 | 55.85 |\n| 8 | 75.87 | 72.90 | 93.00 | 40.44 |\n> Question B) In line 264, what does \"in-scope objects of x\" mean? Specifically, what are the objects, and how do you determine which are in-scope?\n\nI apologize for the confusion our unclear definitions may have caused. We would like to clarify that $\\mathcal{N}(x)$ represents the set of synonyms: in text, it signifies semantic similarities, and in images, it signifies analogous images. For the E-VQA task, each $x_e$ corresponds to one entry as $x_r$ from similar statements. For the E-IC task, we use a manually crafted template to randomly select an entry as $x_r$. The $i_r$ for images are those akin to $i_e$, produced through stable diffusion.\n\n> Question C) For measuring Locality, is there any guarantee that the examples in D_loc-t and D_loc-v are truly unrelated to the edit examples? For example, if you edit the model based on a VQA example from one dataset, if there are similar examples in the other VQA dataset used as D_loc-v then seeing a change there might actually be desirable.\n\nWe wish to emphasize that the data for the locality dataset is chosen from sources external to the training dataset and valid dataset, **ensuring no data overlap**.\n\n>Question D) In the paragraph starting on line 288, what is the \"supplementary data\"? It seems like the description of it is missing; if it is present then this paragraph should be moved or re-written to make it clear what is being referred to.\n\nWe apologize that we didn't articulate the meaning of **supplementary data** clearly here. In this context, **supplementary data** refers to the generality and locality datasets. Our primary argument is that merely a reliable dataset for editing is insufficient; additional datasets are essential to assess the impact of editing. We've clarified this in the revised article.\n\n> Question E) Why are the results split between Table 2 and Figure 5? I would prefer having a complete view of all main results in a single table or figure.\n\nWe presented the generalizability experimental results separately from the main table to offer readers a clearer and more intuitive grasp of the performance in terms of both generality and stability across different editing methods.\n\n>Question F) Is the discussion of SERAC starting at line 483 correct? The model's image generalization seems fine, and I don't think it has \"inherent consideration of M-Locality during the training phase\" because it has terrible performance in M-Locality. Is this just a typo and you meant some system other than SERAC, like KE?\n\nI apologize for the oversight and any inconvenience it may have caused. We've addressed this with a revision in the article.\n\n> Typos Grammar Style And Presentation Improvements\n\nWe've corrected all the spelling and grammatical errors you pointed out in the article. Thank you for highlighting those issues.\n\n[1] [Mass-Editing Memory in a Transformer.](https://arxiv.org/abs/2210.07229)\n\n[2] [Editing Large Language Models: Problems, Methods, and Opportunities.](https://arxiv.org/abs/2305.13172)", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": " - The paper's application of model editing to multimodal LLMs appears to be novel.\n - The authors have provided and will release code and data for their benchmark, allowing the community to use and iterate on it.\n - The paper proposes a way to extend existing model-editing metric concepts to the multimodal setting.", "reasons_to_reject": " - There are a few details that appear to be missing from the paper; see Questions B and D.", "questions_to_authors": "Question A) If I understand the metric equations correctly, the expectations are over cases where you use a single edit example to update the model; if this is the case, is there a way to test a technique's performance when a larger set of edits are made? I could imagine the different techniques having different behaviors as the number of edits increases.\n\nQuestion B) In line 264, what does \"in-scope objects of x\" mean? Specifically, what are the objects, and how do you determine which are in-scope?\n\nQuestion C) For measuring Locality, is there any guarantee that the examples in D_loc-t and D_loc-v are truly unrelated to the edit examples? For example, if you edit the model based on a VQA example from one dataset, if there are similar examples in the other VQA dataset used as D_loc-v then seeing a change there might actually be desirable.\n\nQuestion D) In the paragraph starting on line 288, what is the \"supplementary data\"? It seems like the description of it is missing; if it is present then this paragraph should be moved or re-written to make it clear what is being referred to.\n\nQuestion E) Why are the results split between Table 2 and Figure 5? I would prefer having a complete view of all main results in a single table or figure.\n\nQuestion F) Is the discussion of SERAC starting at line 483 correct? The model's image generalization seems fine, and I don't think it has \"inherent consideration of M-Locality during the training phase\" because it has terrible performance in M-Locality. Is this just a typo and you meant some system other than SERAC, like KE?", "paper_topic_and_main_contributions": "The paper presents MMEdit, a benchmark for evaluating editing of multimodal LLMs. The paper describes the task and three axes of evaluation: whether an editing technique successfully corrects the incorrect outputs (Reliability), whether it handles the edit examples and related examples in a consistent way (Generality), and whether it keeps outputs on unrelated inputs the same (Locality); the latter two are measured separately for the image and text modalities. A few editing techniques and baselines are compared using two backbone multimodal LLMs.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": " - The equations for text locality (line 234) and image locality (line 243) are inconsistent: the first includes the expectation over elements from D_edit but the second does not.\n - Figures 3 and 4  and Table 1 are not referenced in the body of the text; I recommend referencing all tables/figures in the text somewhere, or you run the risk of the reader not looking at them.\n- Line 412-413: \"construct\" -> \"constructs\"\n- Line 428: \"Experiment\" -> \"Experiments\"\n- Line 538: \"Limitation\" -> \"Limitations\"\n- Line 417: x* and y* appear to be undefined."}}, {"review_id": "6WOw5FiPBk", "reviewer": "Reviewer_ysoz", "summary": "", "questions": "Question A: What is the editing prompt in Figure 6?  \nQuestion B: There are other ME techniques that could also be experimented with, say, CaliNet, T-Patcher, KN, ENN, CuQA, SLAG, FRUIT, Prompt-editing, FactTracing, RepairNN, PAINT, Enatailer, GRACE, Fact, and similar. The current set of MEND, SERAC, and KE belongs to the hypernetwork-based approach. But locate-then-edit or parameter-preserving approaches might show better results. One suggestion is to show comparisons with the existing ME techniques. A discussion on the problems, challenges, and possibilities of the existing techniques would help in highlighting the limitations of other editing techniques.\nQuestion C: What would be the hyperparameters or experiment settings? Since the appendix is missing, the exact reproducibility is challenging. It is really good to see that the code and datasets will be made public. The proposed work and problem approach will definitely help the NLP community. \nQuestion D: In Table 2, what are the reasons for BLIP2-OPT to perform worst on reliability and T-Generability, but better on T-Locality and M-locality? The insights and observations from the table are not clear.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your comprehensive feedback. Please find detailed responses in the following.\n\n> Question A) What is the editing prompt in Figure 6?\n\nIKE[1] utilizes demonstrations to guide LMs to edit knowledge facts by in-context learning (ICL). In alignment with the original paper, we choose k-NN examples from the training corpus. The demonstrations are encoded by *all-MiniLM-L6-v2*. Following the default setting, we set k to 32. Due to the extensive length of the prompts in IKE, we only enumerate two representative examples here. \nThe examples for demonstration and target are as follows:\n| Type | Text |\n| -------- | -------- |\n| Demonstration | New Fact: How many balls are on the field? 1\\n Q: How many balls are on the field? A: 1 |\n| Target | New fact: What is the train number? 32678\\n Q: What is the train number? A: |\n\n> Question B) There are other ME techniques that could also be experimented with, say, CaliNet, T-Patcher, KN, ENN, CuQA, SLAG, FRUIT, Prompt-editing, FactTracing, RepairNN, PAINT, Enatailer, GRACE, Fact, and similar. The current set of MEND, SERAC, and KE belongs to the hypernetwork-based approach. But locate-then-edit or parameter-preserving approaches might show better results. One suggestion is to show comparisons with the existing ME techniques. A discussion on the problems, challenges, and possibilities of the existing techniques would help in highlighting the limitations of other editing techniques.\n\nWe acknowledge the breadth and significance of the ME techniques you've pointed out. Indeed, the **locate-then-edit** approach, exemplified by methods such as ROME and MEMIT, focuses on updating facts in the format (subject s, relation r, object o). This method requires pinpointing the location of the last token of the subject within the input before proceeding to the edit. However, in our task, multimodal datasets lack a specific subject to locate, rendering this approach inapplicable.\nRegarding the parameter-preserving approach, the survey[2] indicates that CaliNet exhibits suboptimal performance in single-modality. Consequently, we did not select it as a baseline.\n\n> Question C) What would be the hyperparameters or experiment settings? Since the appendix is missing, the exact reproducibility is challenging. It is really good to see that the code and datasets will be made public. The proposed work and problem approach will definitely help the NLP community.\n\nWe appreciate your interest in the experiment details. The hyperparameters and experimental settings can be found in the **Supplementary Materials**. In our revised manuscript, we will also incorporate these details in the appendix for clarity. Below, we provide the hyperparameters of MEND.\n| Hyper-Parameters | MaxIter | Edit Num | Optimizer  | LR |\n| :----- | ----- | ----- | ----- | ----- |\n| $D_{BLIP2}^{E-VQA}$ | 20,000 | 1 | Adam | 1e-6 |\n| $D_{BLIP2}^{E-IC}$ | 20,000 | 1 | Adam | 1e-6 |\n| $D_{MiniGPT-4}^{E-VQA} $| 20,000 | 1 | Adam | 1e-6 |\n| $D_{MiniGPT-4}^{E-IC} $| 20,000 | 1 | Adam | 1e-6 |\nFurthermore, we will release our code and datasets publicly, aligning with our dedication to contributing to the NLP community.\n\n> Question D) In Table 2, what are the reasons for BLIP2-OPT to perform worst on reliability and T-Generability, but better on T-Locality and M-locality? The insights and observations from the table are not clear.\n\nThank you for pointing out the observations from Table 2. This behavior of BLIP2-OPT can be attributed to the characteristics of certain meta-learning methods. Methods like KE and MEND consider both accuracy and locality in their training objectives. This approach can sometimes lead to trade-offs: doing well in one metric might impact another. When these methods focus on improving locality, they might do better on T-locality and M-locality but might not perform as well on reliability and T-Generability. \n\n> Typos Grammar Style And Presentation Improvements\n\nFor typos and presentation: We appreciate your feedback on improving our paper. We have revisited those sections and have made changes to enhance the overall presentation.\n\n[1] [Can We Edit Factual Knowledge by In-Context Learning?](https://arxiv.org/abs/2305.12740)\n\n[2] [Editing Large Language Models: Problems, Methods, and Opportunities.](https://arxiv.org/abs/2305.13172)", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The presentation of the problem is well understood, and the works highlight the burning need for editing in multimodal LLMs, which definitely adds to aid the NLP community. \n2. The manuscript covers the essential and necessary problem of editing. The insights are definitely helpful for the NLP community. \n3. The set of ME techniques in Table 2 covers standard editing techniques from two paradigms (Hypernetwork-based and parameter-preserving). It shows an insightful observation over two different datasets and four metrics.", "reasons_to_reject": "The reasons for the rejection are as follows:\n1. The proposed problem in the manuscript is no doubt a great contribution, but the insights from the editing techniques and observations are not primarily clear. \n2. An analysis of the performance degradation, for instance, after editing with 10 instances, is not available in the manuscript. \n3. An appendix with the implementation details can help with reproducibility. Additionally, the wall clock time to edit one instance and GPU consumption details are an add-on to show the analysis could also help in the insights. \n4. The manuscript covers the discussion on a range of editing techniques but shows the comparison of editing on four techniques. The discussion and an analysis of the locate-then-edit approach for multimodal LLMs can show better insights with comparable observations.", "questions_to_authors": "Question A: What is the editing prompt in Figure 6?  \nQuestion B: There are other ME techniques that could also be experimented with, say, CaliNet, T-Patcher, KN, ENN, CuQA, SLAG, FRUIT, Prompt-editing, FactTracing, RepairNN, PAINT, Enatailer, GRACE, Fact, and similar. The current set of MEND, SERAC, and KE belongs to the hypernetwork-based approach. But locate-then-edit or parameter-preserving approaches might show better results. One suggestion is to show comparisons with the existing ME techniques. A discussion on the problems, challenges, and possibilities of the existing techniques would help in highlighting the limitations of other editing techniques.\nQuestion C: What would be the hyperparameters or experiment settings? Since the appendix is missing, the exact reproducibility is challenging. It is really good to see that the code and datasets will be made public. The proposed work and problem approach will definitely help the NLP community. \nQuestion D: In Table 2, what are the reasons for BLIP2-OPT to perform worst on reliability and T-Generability, but better on T-Locality and M-locality? The insights and observations from the table are not clear.", "paper_topic_and_main_contributions": "The manuscript proposed MMEdit (Multimodal Model Editing) and presented its evaluations on two tasks: Editing VQA (Visual Question-Answering) and Editing Image captioning. The work highlights that current editing works focus on the textual LLMs, and the authors extend the editing to multimodal LLMs and show the observations that current editing works fall short on the multimodal LLMs and observe a potential gap in the editing techniques by proposing MMedit.  \n\nThe work illustrates the results of two models (BLIP-2 OPT and MiniGPT-4) in editing VQA and Image caption task. The base methods (FT) and Editing (KE, ICE, SERAC, and MEND) results are highlighted with capability on the evaluation of reliability, generality, and locality.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. The link for stable diffusion 2.1 in line 345 is missing. Please check if a link will be suitable.\n2. A gigantic sentence in line 363 can be broken down to help in readability. A grammatical check will fix the grammatical issues."}}, {"review_id": "VeJKAKVs2d", "reviewer": "Reviewer_hZHi", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 1, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your time in reading the paper, and our point-to-point responses to your comments are given below. \n\n> The description in the section of dataset construction is not detailed enough and insufficient to understand how the data was constructed.\n\nWe regret not delving deeper into the dataset construction in Section 3 because of space limitations. We'll endeavor to provide more comprehensive details on dataset construction and aim to make our data publicly accessible, accompanied by more illustrative samples, in the near future.\n\n> The conclusion of the experimental section does not feel very innovative.\n\nWe regret not delving deeper into the conclusion. Space limitations led us to provide a concise summary, but we've detailed most results and rationales in Section 4. We'll strive to enhance this section in upcoming revisions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) The paper introduces a new and challenging task of editing multimodal LLMs, which extends  the existing knowledge editing methodologies for single-modal LLMs.\n2) The authors provide a detailed description of the MMEdit benchmark, which includes metrics for evaluation. This benchmark can facilitate future research in editing multimodal LLMs. \n3) The comprehensive experiments and analysis of the impact of editing different components provide valuable  insights into the editing task.", "reasons_to_reject": "1) The description in the section of dataset construction is not detailed enough and insufficient to understand how the data was constructed.\n2) The conclusion of the experimental section does not feel very innovative.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper focuses on the task of editing multimodal large language models (LLMs), which is  more challenging compared to editing single-modal LLMs. To facilitate research in this area, the authors  construct a benchmark called MMEdit and propose innovative metrics for evaluating multimodal LLM editing. They  conduct comprehensive experiments with various editing baselines and analyze the impact of editing different components.  The results indicate that while it is possible to edit multimodal LLMs to some extent,  the effectiveness is still limited. ", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This papers presents a benchmark aiming at facilitating the evaluation of editing knowledge in multimodal LLMs, specifically the reliability, locality, and generality evaluation metrics are the focus of this paper. Two tasks are included Image  Captioning and VQA. Data and code will be made available.
A general synthesis of the various results would help the reader to have a clear understanding of the results ans insights.
Decision: Accept-Main