Resolving Lexical Bias in Model Editing
Authors: Hammad Rizwan
Domenic Rosati
Ga Wu
Hassan Sajjad
Venue: ICML 2025 poster (2025)
Keywords: Model Editing
Contrastive Learning
Representation Learning
Primary area: deep_learning->large_language_models
TL;DR: PENME tackles the critical vulnerability of model editing techniques by learning a representation space that prevents erroneous edit retrival based on representation similarities.

Abstract: Model editing aims to modify the outputs of large language models after they are trained. Previous approaches have often involved direct alterations to model weights, which can result in model degradation. Recent techniques avoid making modifications to the model's weights by using an adapter that applies edits to the model when triggered by semantic similarity in the representation space. We demonstrate that current adapter methods are *critically vulnerable* to strong lexical biases, leading to issues such as applying edits to irrelevant prompts with overlapping words. This paper presents a principled approach to learning a disentangled representation space that facilitates precise localization of edits by maintaining distance between irrelevant prompts while preserving proximity among paraphrases. In our empirical study, we show that our method (Projector Editor Networks for Model Editing - PENME) achieves state-of-the-art model editing results while being more computationally efficient during inference than previous methods and adaptable across different architectures.

Resolving Lexical Bias in Model Editing
Hammad Rizwan' Domenic Rosati
Ga Wu
Hassan Sajjad
Abstract
46.80%
T5-small
Model editing aims to modify the knowledge of pre-trained language model. Previous ap- proaches have often involved direct alterations to model weights, which can result in model degrada- techniques avoid making modifications to the model's weights by employing an adapter that implements edits through auxiliary components. These rely heavily scoping mechanisms based on distance func- tions on the model's s representation space to de- termine when to trigger edits. We demonstrate that current adapter methods are critically vul- nerable strong lexical biases, leading to is- sues such applying edits to irrelevant prompts with overlapping words. This paper presents principled approach to learning a disentangled representation space that facilitates precise lo- calization of edits by maintaining distance be- tween irrelevant prompts while preserving prox- imity among paraphrases. In our empirical study, we show that our method, Projector Editor Net- works for Model Editing PENME, achieves state-of-the-art model editing results while be- ing computationally efficient during inference compared to previous methods and adaptable across different architectures. We provide the codebase of PENME here: https: //ti:/giiii thub. com/ hww./aaaaaaaaaaaaaaaaa zwan www.a///////...... git
Lexical Bias (%)
T5 pmmmmlllemmee
40-
GPT2-XL GPT2-XLppsrr
LiaMa
LiaMagonme
tion. Recent
10.00%
11.00%
10-
6.40%
2.80%
0.00%
Model (Layer=2)
Figure I. Projector networks mitigate lexical bias: critical prob- lem adapter-based model editing techniques. Percentage of samples where irrelevant but lexically similar prompts closer than semantically similar paraphrases the representation space before and after our learned projection (PENME).
evolves, new information requires updates the models, e.g. the leader of country may change over time.
Periodically updating LLMs using fine-tuning is one poten- tial solution, however, it risks degradation in performance, leading to training from scratch to preserve the model's original performance (Luo et al., 2023; Wang et al., 2025). However, retraining often impractical due to the need of substantial computational resources, time, data, and labor.
Model editing has been proposed sample and compute efficient way to update LLMs (Yao et al., 2023). Histori- cally weight-moddfying techniques that make surgical small parameters updates such as ROME (Meng et al., 2022) have been popular. However, these techniques are known to be computationally inefficient (Yu et al., 2024) and result in catastrophic forgetting (Gupta et al., 2024a), the full impact of which is difficullt to determine (Rosati et al., 2024).
1. Introduction
Large Language Models (LLMs) are successful solving a diverse range of natural language processing tasks (Devlin al., 2019; Liu al., 2019; Touvron al., 2023b; Radford al., 2019). Despite their successes, LLMs are fallible in large part due to the noisy and imperfect nature of the data used for training (7hu et al., 2020). Moreover, as the world
Weight--ressering approaches solve these problems by maintaining the original model parameters and use ad- ditional components like key-value codebook adapters (GRACE and MELO) (Hartvigsen et al., 2023; Yu al., 2024) that apply a scoping mechanism to determine whether to trigger model edit, using distance function over model representations to assess semantic similarity between the input and an edits registered in a codebook.
Department of Computer Science, Dalhousie University, Hal- ifax, Canada. Correspondence to: Hammad Rizwan <ham- mmd.rizwanndaa.ca...
We find that the performance of preshhtpreeerving meth- ods is heavily reliant on scoping mechanism which suffers from critical vulnerability of Lexical bias (Figures and
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Resolving Lexical Bias in Model Editing
The twin city of Pintsburhh
The twin city of Pittsburgh
The twin city of Pittsburgh is
The twin city of Portsmouth is
The twin city of Portsmouth is
The twin city of Portsmouth is
Edits
Inclevant Prompts
Paraphruses
Similarity Threshold
Pittsburgh is a twin city
Pittsburgh twin city of Pittsburgh is a twin city of
Distance
b)
c)
Figure 2. An illustration of lexical bias in embeddings: a) a low similarity threshold (illustrated with the circle) results in failing to edit paraphrases. b) A high similarity threshold results in misfires with irrelevant prompts. c) illustrates our solution which restructures the representation space.
2). prompts with similar lexical tokens but different seman- tics that are closer together in the representation space compared to a prompt and its respective paraphrases. Lex- ical bias prevents current adapter-based methods from ef- fectively being able to balance generalization to unseen paraphrases and "misfiring" on semantically dissimilar (ir- relevant) prompts. Our analysis ($ 1. Figure 4) of these misfires reveals that the representation space used calcu- late semantic similarity is dominated by lexical bias where irrelevant prompts (e.g. "The twin city of Portsmouth is") are often closer by euclidean distance to input (e.g. "The twin city of Pittsburgh is") than a true semantically similar prompt (e.g. "Pittsburgh is a twin city of").
2024atc; Mazzia et al., 2024; Durrani et al., 2025). Here, we summarize three main themes of model editing.
modift-go approaches apply targeted updates to the model's weights to reflect new knowledge (Tanno et al., 2022; Meng et al., 2022; Gupta et al., 2023; Xu et al., 2023; Hase et al., 2023b; Li et al., 2024b; Fang et al., 2025; Ma et al., 2025). Fine-tuning on edited knowledge can have a detrimental impact on model's general capabilities; there- fore, many methods instead perform targeted updates. These approaches typically rely on the localization hypothesis (Miller et al., 2016; Geva et al., 2021) in the Transformer architecture, which conjectures that the point-wise feed- forward components act as a key-value memory for infor- mation retention within an LLM, claim that has recently been challenged, in the context of model editing by Hase et al., 2023a. Meng al. (2022; 2023, ROME, MEMIT) identifies salient neurons within the feed-forward layers, facilitating targeted updates to effect the desired edits us- ing causal analysis. Similarly, Li et al. (2024b, PMET) investigates the role of multi-headed attention, in conjunc- tion with feed-forward layers, for model editing. As we mentioned earlier, these methods suffer from general model degradation due gradual performance drift, which can lead to catastrophic forgetting (Gupta et al., 2024a). Im- provements on MEMIT have been made by Ma et al. (2025, PRUNE), which bounds the condition number of the edited weight slice so that successive edits cause only minimal drift and the model's overall behaviour stays intact. Simi- larly, Fang et al. (2025) propose AlphaEdit, which projects each update into the null space of knowledge that must re- main unchanged. This preserves existing knowledge while inserting the update. AlphaEdit showcases the strongest per- formance; however, the method is evaluated on mini-batche of 100 edits, reducing the batch size to for sequential edit- ing, this necessitates recomputing the projection for every edit, increasing computational cost and is likely to lead to performance drift during prolonged editing.
Based on this analysis, we propose Projector Editor Net- works for Model Editing (PENME), an advancement over adapter-based based model editing that explicitly targets the lexi- cal bias problem by learning a projection that disentangles lexically similar and semantically similar text representa- tions. We empirically demonstrate that resolving lexical bias enables high edit generalization performance across paraphrases of prompt while also achieving strong locality that prevents irrelevant prompts from triggering edits.
Our contributions are follows: (1) We show that represen- tations extracted across layers exhibit lexical bias, showing a bias towards token overlap, which introduces significant challenges for adapter-based model editing techniques. (2) We propose PENME, a model editing framework that learns a projection network that maps the model's 's representation space to a new representation space where lexical bias is minimized. (3) We integrate our projection network in adapter-based retrieval scheme for model editing, demon- strating, for the first time in adapter-based approaches, high efficacy in both paraphrase execution (generalization) and prevention of misfires on irrelevant prompts (locality).
2. Related Work
There are number of surveys that provide comprehensive overview of various model editing methods (Wang et al.,
An alternative approach, using hypemneworr,, is Mitchell et al. (2021, MEND), which predicts new model weights by
2

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Resolving Lexical Bias in Model Editing
generating low-rank decompositions of the weight matrices across different layers.
and adversarial stress tests (Nie al., 2020), recent work shows the bias remains stubborn (Serrano et al., 2023). No- tably, our work is the first to highlight this issue in model editing and propose a solution to mitigate it.
Weight-preserving pre-input approaches depend on ex- tracting and processing relevant edit information before the input is processed by the main model Mitchell et al. (2022); Zheng et al. (2023); Zhong et al. (2023). For example, SERAC (Mitchell et al., 2022) employs a memory- model editing strategy augmenting with a memory storage and supplementary models to determine the scope of the edit. RAG-based methods like IKE (Zheng et al., 2023) leverage similarity- based retrieval to extract and rank edit demonstrations from memory and use in-context reason- ing to edit. A limitation of these approaches is the com- putational overhead as they require additional models for ranking, relevancy, context processing and generalization.
3. Problem Setting: Model Editing
The aim of model editing is alleviate the need for com- plete retraining when updating learned knowledge. Editing attempts to satisfy the following conditions: (1) sample effi- ciency: update the model with the fewest number of samples possible, (2) compute efficiency: train a small portion of the model only, (3) minimal impact: make as small of an impact on unrelated behaviour as possible i.e. prevent misfires on irrelevant prompts and (4) ensure generalization: maintain accurate paraphrase behaviour i.e. retrieval of correct edits in adapter- approaches.
Weight-preserving post-input These rely the model's in- ternal representations to implement scoping mechanisms, de- termine whether a specific edit applies for the current input. If an edit does apply, they employ a playback mechanism such as representation vector addition or replacement that results in the model generating updated outputs Hartvigsen et al. (2023); Yu et al. (2024); Lee et al. (2022). Most meth- ods employ a key-value codebook: vector representation of the inputs which should be edited are stored as a key and the representation vectors of the desired edit phrase are stored as a value. Semantic similarity is computed using a distance metric, such as Euclidean distance, against future inputs to the language model. If a distance threshold is satisfied, then the edit vector is "played-back."
The goal is to modify the behaviour of a model M on a dataset D = [d1, ..., dn] where the sample d; is the tuple (zz: Vr. [pa..... Pinl. (P............ P...). z; is the edit prompt, yi is the new output tokens, 1mm are a set of paraphrases of the edit prompt x; and pi are irrelevant prompts, examples that are both lexically and semantically related; however, they represent cases where the underlying model's genera- tion output should remain unchanged. For instance, consider the edit "What is the twin city of Detroit". A lexically sim- ilar prompt would be "What is the twin city of London", whereas semantically related prompt might be "For Detroit, tell me what twin city it has" where the semantic relation- ship lies in the fact it is a paraphrase. For successful model editing, the edited model, M', should generate new target tokens y/ for specific input z; (Edit Success) and its re- lated paraphrases Pi... (Generalization), while maintaining the model's behaviour on semantically unrelated prompts Pi:n (Locality). The following metrics illustrate how these factors are typically operationalized (see for example Yao et al., 2023; Yu al., 2024; Hartvigsen et al., 2023; Gupta et al., 2024b).
Alternativ strategies introduce lightweight auxiliary struc- tures-either additional neurons to scope and steer model outputs, as in Huang et al. (2023); Zhu et al. (2024), fixed- size hook layers that accumulate residual updates for con- secutive batch edits while keeping the base weights frozen Li et al. (2024a), purpose-buil built external memories Wang et al. (2024b, WISE), or compact jet-pack modules (Sutton et al., 2024) that modify hidden activations for specific prompt, originally proposed for stealth edits.
Edit Suecess (ES): The proportions of edits that the model is able to recall or generate correctly, also referred as effi- cacy, reliability, and edit score. Formally we say (xx)) = yi, V(z,, 36) E dim-.
Lexical Bias Hartvigsen et al. (2023, GRACE) employs playback vectors as above, whereas Yu et al. (2024, MELO) utilizes LoRA blocks. We find that both of these methods are vulnerable lexical bias which invalidates their retrieval methods. In order to resolve this issue, current methods need to hand-engineer distance thresholds which balance the trade off generalization for irrelevant prompt misfiring protection. Our method, PENME, resolves lexical bias by learning a new disentangled representation space where large thresholds can safely be used that are much more re- liable at preventing misfires. While lexical bias has been repeatedly documented through initial diagnoses of syntac- tic and lexical heuristics (McCoy et al., 2019; Dumpala et al., 2024), dataset-level analyses (Zhou& & Bansal, 2020),
Locality: The proportion of irrelevant prompts for which the model generates the same outputs prior editing, also referred to specificity, neighbourhood success, retain rate and neighbourhood score and is denoted as: M' (pT)) = MPT)), Vp,jj E dimn-
Generalization:
The proportion of paraphrases for which the model is able to recall or generate the correct edited in- formation, also described as paraphrase success: M' M((P.)) yi, Vp, E Ps. (pc, Ui) d1:n-
3

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Resolving Lexical Bias in Model Editing
Projector Network
Feod Forward Block
Feed Forward (FFN)
Attention Block
Keys
Values
(Representations) (Thresholds, Outputs)
Similarity Thresholds Edits
Incelvant Prompts Paruphrases
Transforner Block
Key-Value Codebook
Figure PENME uses projection network that interfaces with the pointwise feed-forward layer output in transformer block. The projection network, coupled with key-value codebook storage, acts scoping mechanism by comparing projection outputs with codebook entries. This determines whether the current input relates to a specific edit or should pass through the model unmodified.
Score: It is the mean of the above three metrics and is used for benchmarking.
edited inputs than irrelevant prompts. Our training loss is inspired by contrastive learning (Hadsell et al., 2006) and defined by the following loss function:
4. Projector Editor Networks for Model Editing (PENME)
L x) 11- 100 =
+ 1 [axx (0,m m 1 -
PENME is a an adapter module which integrated after the pointwise feed-forward layers within a transformer block of pre- trained LLM. By introducing this additional component rather than altering the original model weights, PENME enables the integration of new information while preserving the LLM's initial capabilities.
model editor that leverages
(1)
1, if Pij.
=
0, if PT V
where t is the target {0, 1} which is O when the training pair is {.i, Pky) (edit, paraphrase) and when the training pair is {æ,, Pij (edit, irrelevant) or the inter-edit (or edit-to-edit) pair (x,,,;] where we sample an unrelated edit, m the margin which pushes Pij at least m distance away from x;. The projection network is trained such that for all samples in dataset, edits x; and edit paraphrases Pij close together while edits and irrelevant PiP paraphrases or unrelated edits 2y are pushed apart in the projection space. Training is performed by sampling pairs at random. Note that is a variable that is assigned either paraphrase, an irrelevant prompt, or an unrelated edit just as way make the loss function more concise.
PENME, illustrated in Figure consists of two compo- nents: (I) Projection Network (g) projects model activa- tions denoted hy (énpptt) layer / into a distinct representa- tion space g(hy (input)) ) (2) Key-Value Value Codebook stores the projected model activations g(1 (input)) at layer / as keys and corresponding values containing a learned similar- ity threshold (6) and the new associated output information y:- This paper only considers storing strings as yi. but vec- tors (Hartvigsen et al., 2023) or LoRA block indices (Yu al., 2024) can also be stored as values, which facilitate playback approaches.
The inherent lexical and semantic similarities among edits increase the probability of certain edit paraphrases exhibit- ing greater proximity to other unrelated edits. This phe- nomenon can lead to erroneous paraphrase-ed associations during execution, potentially triggering inappropriate edit operations. This is why also push unrelated edits farther away in Eq. 1 as well unrelated prompts. These pair- ings are formed based on similarity threshold defined as hyperparameter Φ.
In the following sections, the vectors 27j. Pīj. and P.j are outputs of the projection network gin((nn) for dataset components Fi, Pij and Pij.
4.1. Projection Network
We hypothesize that if the representation space suffers from lexical bias, then we could learn a new representation space that disentangles lexical and semantic representations. We achieve this by training a projection network g(-) d >> g using contrastive learning whose function to project inputs into space where paraphrases of inputs closer to
The projector network is a 2-layer MLP with one ReLU non-linearity and batch norm applied between each layer. The dimensionality of each layer the same as the original

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Resolving Lexical Bias in Model Editing
representation space. Note that this network is only applied to one single layer. The compact architecture of the projec- tion network enables it to be trained on GPUs with limited memory capacity since we can amortize the computation of representation vectors (denoted above with the vēc symbol beforchand) irrespective of the underlying model's scale. We provide the details of implementation, data construction and training in Appendix A.
possibility is to set the threshold based on close irrelevant prompts Min(Il (rjlla) T; this option would maintain locality by preserving all training irrelevant prompts.
4.4. Analysis of Codebook Management and Scalability
Both GRACE and MELO require multiple paraphrases added to the codebook to improve generalization. The PENME codebook scales linearly with the number of edits, as each edit corresponds to a single codebook entry. Main- taining one entry per edit enables efficient edit removal or updates, providing greater flexibility in edit management. In contrast, the scoping mechanism employed by Hartvigsen et al. (2023); Yu et al. (2024) deal with multiple, possibly conflicting, entries per codebook requires splitting and merg- ing operations. The effectiveness of this approach varies across datasets, For instance, for GRACE, the zsRE dataset exhibits a high occurrence of similar edit outputs (same entity with the same edit), allowing for substantial reduc- tions in codebook entries. Specifically, 1,000 edits on zsRE require only 658 entries, whereas the Counterfact dataset requires 1,682 entries for just 300 edits. The combination of this consolidation process and the potential for edits to be closely related in vector space leads to overlapping cluster radii, necessitating cluster size reduction. This inadvertently results in the removal of certain edits. A detailed comparison between PENME and the scoping mechanisms employed by GRACE and MELO is presented in Appendix C. The results demonstrate that PENME achieves superior edit retrieval speed and highlights the problem of edit conflict and edit forgetting by GRACE and MELO.
4.2. Key-Value Codebook
The key-value codebook is a memory mechanism designed to store edits and their corresponding outputs. For each edit, representations are generated by passing the input zt through the model and the projection network, denoted as E = (z)) 2i are then stored as keys k E K in the codebook and are utilized during runtime in a similarity- based retrieval system to access the relevant edit. The code- book value v. E V consists of the edited information j along with a similarity threshold 6. The edited information in this paper is an exact string stored from the model editing dataset. The threshold serves scoping mechanism and is learned using procedure described below. For given input prompt 2;, euclidean distance| || 1l2 is computed with all keys in the codebook. From the computed distances, we determine if the input prompt x; is relevant to the edited codebook value of and its corresponding threshold of. This expressed as:
arg min ||=F;- kg ll K K
S.L. |, - kill2 < uf
If the prompt x, is deemed relevant (Equation 2), the out- put information of the edit is retrieved from codebook of" Otherwise, the typical model output M (a;) is employed.
5. Experimental Setup
We assess the performance of PENME across a spectrum of transformer-based I. LLMs,, including Text-to-Texx Transfer Transformer (specifically T5-small) (Raffel et al., 2020), Llama-2-7b (Touvron et al., 2023a) and GPT2-XL. (Rad- ford et al.. 2019). We compare PENME with GRACE and MELO, as these are the only other current weight-preserving adapter based methods. Additionally, we include MEMIT and SERAC' Working details of the methods and hyper- parameters are provided in Appendix D.1. We utilize the methodology outlined in $6.1 to select optimal layer to introduce PENME adapter and use the second layer for all models. We determine the optimal threshold for each edit by systematically varying the 7 parameter in Eq. (3) across a range of 0.05 to 0.20.
4.3. Finding the thresholds of and
Initial experimental findings regarding the thresholds of reveal that unseen test paraphrases typically demonstrate greater distance than the average seen training paraphrases, while the inter-paraphrase distances within the training set exhibit variation across edits. In contrast, unseen test ir- relevant prompts generally show closer proximity to edits compared to the nearest seen training irrelevant prompts. This effect is illustrated in greater detail in Appendix B. We determine an appropriate threshold by utilizing data-driven thresholding scheme based on the training data:
Max (1 pīll22) +
(3)
The threshold is determined as the maximum paraphrase distance observed for each individual edit, augmented by hyperparameter 7 to account for unseen paraphrases. We select a value of r through grid search. This formulation al- lows our method to achieve an optimal balance between gen- eralization and locality preservation. Alternatively, another
Dataset The zsRE dataset (Levy et al., 2017) and the Counterfact dataset (Meng et al., 2022) are the most com- monly used model editing datasets. zsRE consists of an
'A simpler version of SERAC is used in Hartvigsen et al. (2023) called Defer.
5

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Resolving Lexical Bias in Model Editing
50
T5-small GPT2-XL
6.1. Lexical Bias
Llamn-2-77
To examine the lexical bias of representations, we randomly sampled 500 entries from the Counterfact dataset (see $5), For each entry, created triplets consisting of an edit prompt, a randomly sampled paraphrase prompt and an ir- relevant prompt with high lexical overlap (a;, (C,,Pi;P) P,) In Table 7, we see the ROUGE-1 token overlap w.r.t ay mean FI was 0.3 for both p. and p; Qualitative samples are provided for the reader's validation in Table 8. These triplets are fed into various models, and representation vectors (di, Pi, P, from the ferd-forward block of each layer / are extracted. We select either averaged token representations or dedicated sentence representations, based on whether a given model offers a specific token for sentence-level representation. We calculate two sets of pairwise Euclidean distances: (1) Be- tween edit representations and paraphrase representations: || pilla (2) Between edit representations and irrelevant prompts representations: ||=;; p; 22- We then compare these distances to determine if irrelevant prompts are closer to the edits than the paraphrases ||قž pill2 > || P. lle. Figure 4 displays the percentage of samples where irrelevant prompts were closer to the edits.
40
20
10
Leyer ID
Figure 4. Percentage of samples where edits are closer to lexically similar yet irrelevant prompts as compared to paraphrases in the representations space of different models across various layers. TS-small, GPT2-XL and Llama-2-7b have 6, 32, and 48 layers, respectively. The full figure for all layers is presented in App. E.I.
edit prompt along with several paraphrased versions of that prompt. Irrelevant prompts are sourced from the NQ dataset (Kwiatkowski et al., 2019), which offers a wide range of user query questions. In contrast, Counterfact has similar edit and paraphrase prompts but employs more nuanced approach to irrelevant prompts. It includes prompts that are similar the edit prompt in both semantic nature and lexical structure. This differs significantly from zsRE, where the irrelevant prompts are neither semantically nor lexically related to the edit prompt. Moreover, zsRE has a lower diversity in subjects, relationships, and linguistic variations (Meng et al., 2022). This structural difference between the datasets has important implications for eval- uation. In zsRE, the lack of semantic or lexical relation- ships between the edit prompt and its irrelevant prompts allows weight-preserving ity scores with relative ease. The enhanced complexity of Counterfact renders it a more robust benchmark for evalu- ating editing mechanisms. Dataset processing and training data construction details are provided in Appendix D.2.
Figure 4 reveals an intriguing pattern: except for the first layer in most models, the early layers demonstrate reduced percentage of samples where irrelevant prompts are closer to edits than paraphrases. However, the trend shifts as we progress through the model's depth. In the mid-layers, this percentage begins to ascend once more, only to descend slightly towards the final layers, albeit with subtle fluctua- tions among them. We hypothesize that the initial layers, token-specific information remains largely isolated. How- ever, as the input traverses deeper into the model, guided by repeated attention mechanisms, this information becomes amalgamati across tokens (Sajjad et al., 2022). Moreover, repeated normalization as demonstrated by Takase et al. (2022) results in smaller changes in weights of an LLM, leading to embedding vectors in the final layers being simi- lar, thus only subtle fluctuations are seen in the percentages.
approaches to achieve high local-
These results indicate why there is significant chance of misfire in adapter-based methods: lexical bias. This also provides systematic approach for identifying the optimal layer to introduce PENME integration by elucidating the regions within the model's architecture where lexical bias exhibits minimal influence. Although the projector network approach can be generalized across all layers, demon- strated in Appendix E.2, it is advantageous in terms of training time to integrate at points of minimal influence.
We adopt the evaluation setup of Ma et al. (2025) to assess downstream performance. Specifi- cally, we evaluate using three tasks: sentiment classification using the DAiR-Emotions dataset (Saravia et al., 2018), summarization using the CNN//DallyMail dataset (Hermann al., 2015). and natural language inference (NLI) using the RTE dataset (Dagan al., 2005).
Downstream Tasks
6. Evaluation
6.2. Disentangled Projection Space
This section presents the evidence of lexical bias, the results of PENME in achieving separability of irrelevant prompts and paraphrases, and a comparison with other methods.
In this section, we validate proposed projection network in its ability to learn generalized disentangled representa- tion space where paraphrases are closer to edits as compared
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Resolving Lexical Bias in Model Editing
Table A comparative analysis of PENME and model editing methods on 2000 edits from the Counterfactual dataset and 1000
edits on zsRE. The metrics are Edit Success (ES), Locality (Loc) and Paraphrase Generalization (Para).
COUNTEREACT
ZsRE
Method
Model
Para
Score
ES
Loc
Para
Score
PENME
T5-small
1.000
0.787
0.808
0.865
1.000
0.941
0,913
0.951
Llama-2-7b
1.000
0.869
0.906
0.925
1.000
0.987
0.966
0.984
GPT2-XL
1.000
0.847
0.875
0.907
1.000
0.957
0.940
0.966
MELO
T5-small
0.850
0.800
0.037
0,562
0.990
0.640
0,986
0.872
GPT2-XI.
1.000
1.000
0.020
0.673
1.000
0.004
1.000
0.668
GRACE
T5-small
1.000
0.860
0.140
0.667
1.000
0,730
0,993
0.907
Llama-2-7b
1.000
0.997
0.002
0.666
0.100
0.591
0.000
0,230
GPT2-XI.
1.000
0.996
0.003
0.666
0.992
1.000
0.010
0.667
SERAC
T5-small
0.017
0.526
0.010
0.184
0.017
0.526
0,010
0,184
Llama-2-7b
0.992
0.372
0.651
0.672
1.000
0.114
0.357
0.490
GPT2-XL
0.947
0.669
0.408
0.675
0.474
0.003
0.811
0.429
MEMIT
Llama-2-7b
0.147
0.149
1.000
0.432
0.402
0.002
1.000
0.468
GPT2-XL
0.785
0.788
0.502
0.692
0.214
0.000
1.000
0.405
FT
T5-small
0.955
0.000
0.450
0.468
0.017
0.526
0.010
0.184
Llama-2-7b
0.404
0.393
0.417
0.405
0.569
0.020
0.746
0.445
GPT2-XL
0.968
0.851
0.395
0.738
0.608
0,005
0.889
0,501
PENM Atrram
T5-small
1.000
0.782
0.756
0.846
1.000
0.615
0.550
0.721
1l-ma-2-7b
1.000
0.871
0.818
0.896
1.000
0.716
0.792
0,836
GPT2-XL 1.000
0.850
0.768
0.872
1.000
0.733
0.768
0.833
to irrelevant prompts. We sample 1500 tuples Pei,Pi,7 PT) of edits denoted e,, paraphrases ps, and their unrelated irrele- vant prompts pT from the Counterfact dataset with accompa- nying input prompts 2; and split them into train and test sets of 1000 and 500 samples respectively. We use the training set to train the projector network using model representa- tions from layer of each model. To evaluate the network's s performance, we compare two types of test representations: the original model representations hx(x;) where x; is the input prompt and the projected representations (ara)) This comparison uses the experimental method described earlier, allowing us to determine whether the projection net- work successfully learns to create a lexically disentangled representation space.
6.3. Model Editing Results
Table 1 presents the comparative results of PENME and recent model editing methods for 2000 edits on the Counter- fact dataset and 1000 edits zsRE. PENME demonstrates stable performance across editing metrics compared to other model editing approaches. In particular, PENME shows high efficacy on both locality and generalization and has stable performance across different models. Observe that for both GRACE and MELO, these methods require trading of locality for paraphrase performance or vice versa due lexical bias.
GRACE, similar to PENME, demonstrates high edit success rates due to its inherent design. However, its generalization scores compared to PENME were markedly low, suggesting poor performance on edit paraphrases post-editing. GRACE achieved the highest locality scores, with T5-small at 0.92 and Llama-2-76 nearly perfect at 0.997. The substantial difference between locality and generalization scores be attributed to GRACE's of very low distance threshold, resulting in poor performance on paraphrases but success- fully avoiding irrelevant prompts spillover into edits.
The results presented in Figure demonstrate that the pro- jector network effectively learns distance lexically simi- lar but unrelated irrelevant prompts in comparison to para- phrases. A two-dimensional PCA visualization of the repre- sentation space, illustrating this phenomenon, is provided in Appendix F.2.
For data pairs where irrelevant prompts are closer to edits than paraphrases, T5-small exhibits a dramatic decrease from 46% 6.4%. Similarly, GPT2-XL reduces from 10% 2.8%, and Llama-2-70 drops to 0% from 1 11%, indicating perfect separability of irrelevant prompts and paraphrases.
SERAC also achieves a high edit success but shows mixed performance results for generalization and locality across models. For T5-small, the approach does not work well as
Due to computational constraints, editing is performed on 1,000 zsRE samples. However, this number is consistent with the typical sample size used in related literature.
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Resolving Lexical Bias in Model Editing
0.95
and Gangaaltaak
Genoratzatenn Localty
0.90
Edit Pairings Generalization
0.90
0.85
Locality
0.85
0.80
0.75
0.75
0.70
0.65
0.60
0.025
0.250
0,075
0.300
0.125
0.550
0.175
0.200
0.60
0.025
0.050
0.075
0.100
0.225
0.350
0.375
0.200
Threshold
Threshold
different hyperparameter settings. The distance threshold T varies from 0.01 to 0.2 (0.01 increments and T is normalized by 100), while the edit-pairing similarity threshold á ranges from 0.5 to 0.9 (0.1 increments). Higher á values enforce stricter edit similarity requirements. The results showcase the effect of hyperparameter tuning on the projector network's 's learning capacity and overall performance.
Figure 5. Shows the off between generalization and locality performance
SERAC uses logically entailed facts determine the scope, the original work uses TS-large which is significantly better at reasoning.
Locality Generiliation
0.9
0.8
For GPT2-XL, MEMIT demonstrates moderate effective- ness, achieving an edit success rate of 0.785 and local- ity score of 0.788. In contrast, when applied Llama- 2-7b, both the edit success and paraphrase success rates relatively low, although the locality score remains high.
0.7
Liama Paraphrase Success
Llama Locality
0.6
GPT2-XL Paraprrase Success
GPT2 XL Locality
0.5
Paraphrase
T5-maal Locality
This discrepancy is likely due to challenges stemming from MEMIT's training on the Llama-2-7b model, as similar findings have been reported by Huang et al. (2024).
1000
Number
Figure 6. PENME's performance in terms of L Locality (dotted) and Generalization (continuous line) across varying numbers of edits
Projector Generalization In the previous setting, we trained the projector using all of the edit samples at once i.e. batch editing. In this setting, we evaluate the stream lifelong editing setting for zero-shot generalization, where we update the codebook once per edit using frozen projec- tor. As trained projection network is needed we initialize PENME using 2k unseen samples from Counterfact. Table presents the results. Analysis on the zsRE dataset demonstrates the projector network's capacity for zero-shot generalization, achieving robust performance metrics while maintaining equilibrium between generalization and local- ity. For the Counterfact dataset, the drop in performance is minimal with the slight exception on the generalization of GPT2-XL. These findings suggest that training a projector network on a more extensive dataset to reduce lexical bias could enable its use as a modular component.
generalization and locality compared other models. We hypothesize that this under-performance may be attributed to either the model's smaller size, resulting in less robust learned representations. or the fact that it was trained on a more limited dataset relative to larger, more recent mod- cls. Projection networks trained on Llama-2-7b and GPT2- XL representations exhibit comparable performance levels. Both models show slight decrease in generalization and locality performance the number of edits increases from 1000 to 2000, with minimal decline after that.
Examination of projection network behaviour reveals inter- esting patterns in generalization and locality failures based on the varying distances between training edits and their respective paraphrases and irrelevant prompts after the train- ing of the projector network. The varying distances result in different thresholds for each edit, which can cause er- rors when the closest edit to irrelevant prompt example has a high threshold. To quantify these observations, we employed ROUGE scores a comparative study of gener- alization outcomes. Appendix G provides further analysis of the learned projection space.
6.4. Scaling Edits
We evaluate the projection network's stability under varying numbers of edits using incrementally larger training sets ranging from 1000 to 5000 edits, with 1000-edit increments per training session. The results of the experiment shown in Figure 6. Projector network trained representations from T5 mmlll demonstrates lower overall performance in
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Resolving Lexical Bias in Model Editing
Model
NLI
Sentiment
Summarization
preserves model general capabilities.
Llama-2-7b 0.6476
0.6573
0.1865
Haama-2-7bpanaa
0.6428
0.6573
0.1865
9. LLong Form Generation
GPT2-XL
0,5128
0,4630
0.0936
GPT2-XLPENME 0.5128
0,4630
0.0936
As discussed in Section 4, vector playback or trainable LoRA blocks can be used to support long-form generation. In this section, we adopt the retrieval-based prompting strat- egy introduced by IKE (Zheng et al., 2023) to perform long form generation. Since PENME operates on early layers of the model, inference can be halted early when user input falls within the scope of an edit. In such cases, the edited information is retrieved and used to construct new prompt, combining the retrieved content with the user query to guide the model's 's generation.
Table Downstream task performance of GPT2-XI, and Llama-2- 7B before and after PENME editing, demonstrating the method's effect on model general capabilities.
Model
Fluency
Reference Score
Llama-2-7b
611.54
16.57 622.36
21.98
Table 3. Evaluation of long-form generation by Llama-2-7B pre- and post-editing with PENME (via IKE).
To evaluate this approach, metrics which include Gen- eration Entropy (Fluency) and Consistency (Reference score) (Meng et al., 2023) are used. Table 3 presents the results of this approach on the CounterFact dataset. We ob- serve an improvement in fluency of the model's generation (fluency), indicating a reduction in repetitive or redundant output, and a corresponding increase in reference score, re- flecting better factual alignment. Sampled generations and detailed analysis along with a discussion on multi-hop edit- ing, is provided in Appendix I. The samples demonstrate that the edited fact is consistently preserved and integrated across the full output.
7. Generalization and Locality
To demonstrate the trade-off between generalization and locality, we conducted an ablation study by varying the r pa- rameter, which modulates the similarity threshold defining an edit's s scope. Figure 5 presents the results for GPT2- XL and T5-small. The trends observed for GPT2-XI. and ---ma-2-77 are similar. Therefore, for clearer visualization, we present the detailed results for 1lama-2-7b separately in Appendix F.I. Setting a low 7 value achieves near-perfect locality but poor generalization. As we incrementally in- crease the threshold, generalization improves while locality declines gradually. Each model exhibits an optimal thresh- old where generalization and locality are balanced; these thresholds can be adjusted to suit specific use cases e.g. high locality to ensure no degradation in the original model.
10. Conclusion
In this paper. we raised awareness of a critical vulnera- bility in weight-preserving adapter-based based model editing techniques: lexical bias in the representation space. We developed a projection projection-based method PENME trained via contrastive learning to disentangle lexical and semantic sim- ilarity which originally would cause misfiring on irrelevant prompts with high lexical overlap. Empirical evaluations showed PENME's superior performance across varying lev- els of task complexity. On the zsRE dataset, it achieved im- pressive generalization and locality scores exceeding 0.90, demonstrating that our method is satisfactorily able to bal- ance generalization and locality using distance metrics in this new projected space. Notably, when assessed on the more challenging Counterfact benchmark, the system main- tained robust performance, attaining scores above 0.80 for both generalization and locality metrics. This performance Counterfact is particularly significant given the bench- mark's 's increased difficulty, underscoring PENME's efficacy. In future work, we aim to investigate whether projector pre- trained on a large-scale dataset can serve as a plug-and-play component for cross-lingual generalization. Additionally, we plan to explore whether the projector can be trained and updated incrementally with new edits, thereby reducing training overhead and improving scalability.
Figure also illustrates the impact of varying the similarity threshold for edit- to-edit pairings in the training dataset on the projector network's learning. Edit-to-ed to-t pairings & which move edits farther away from each other are central to training a robust projector network. The threshold value for edit-to-cdit pairings è significantly impacts training stability and performance. Higher thresholds, such as 0.75, result in fewer pairings and lead to unstable training for both Llama- 2-7b and GPT2-XL models, ultimately resulting in poor performance. Conversely, lower thresholds, exemplified by 0.6, increase the number of pairings and enhances stability.
8. Downstream Task
Table 2 reports the performance of GPT2-XL and Llama-2- 7B across downstream NLP tasks, before and after applying PENME. For GPT2-XL, performance remains unchanged post-edit, indicating that the edit was successfully localised without negatively affecting general capabilities. Similarly, Llama-2-7B exhibits stable performance on summarization and classification tasks, with only a minor drop observed on the IIJ task. These results suggest that PENME effectively
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Resolving Lexical Bias in Model Editing
Impact Statement
in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 Novem- ber, 2021, pp. 5484-5495, Association for Computational Linguistics, 2021.
This paper advances model editing by mitigating lexical bias in adapter-based approaches, enabling precise and tar- geted updates to language models. While our method does not introduce an additional ethical risk beyond those al- ready associated with language models, the model editing techniques, in general, can be exploited to inject unsafe behaviour into model (Li et al., 2024c).
Gupta, A., Mondal, D., Sheshadri, A. K., Zhao, W., Li, X., Wiegreffe, S., and Tandon, N. Editing common sense in transformers. In Bouamor, H.,, Pino, J., and Bali, K. (eds.), Proceedings of the 2023 Conference on Empiri- cal Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023, pp. 8214-8232. Association for Computational Linguistics, 2023.
Acknowledgment
We acknowledge the support of the Natural Sciences and Enginecring Research Council of Canada (NSERC), Canada Foundation of Innovation (CFI), and Research Nova Scotia. Advanced computing resources are provided by ACENET, the regional partner in Atlantic Canada, and the Digital Research Alliance of Canada.
Gupta, A., Rao, A., and Anumanchipalli, G. Model editing at scale leads to gradual and catastrophic forgetting. In Ku, L.., Martins, A., and Srikumar, V. (eds.), Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, pp. 15202-15232. Association for Computational Linguistics, 2024a.
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Yao, Y., Wang, P., Tian, B., Cheng, S., Li, Z., Deng, S., Chen, H., and Zhang, N. Editing large language models: Problems, methods, and opportunities. In Bouamor, H., Pino, J., and Bali, K. (eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6- 6-10, 2023, pp. 10222-10240. Association for Computational Linguisties, 2023.
Yu, I.., Chen, Q., Zhou, J., and He, I.. Melo: Enhancing model editing with neuron-indexed dynamic lora. In Proceedings of the AAAI Conference on Artificial Intelli- gence, pp. 19449-19457, 2024.
Zheng, C., Li, L., Dong, Q., Fan, Y., Wu, Z., Xu, J., and Chang, B. Can we edit factual knowledge by in-context learning? In Bouamor, H., Pino, J., and Bali, K. (eds.), Proceedings of the 2023 Conference on Empirical Meth- ods in Natural Language Processing, EMNLP 2023, Sin- gapore, December 6-10, 2023, pp. 1862-4876. Associa- tion for Computational Linguistics, 2023.
Zhong, Z.. Wu, Z., Manning, C. D., Potts, C., and Chen, D. Mquake: Assessing knowledge editing in language models via multi-hop questions. In Bouamor, H., Pino, J., and Bali, K. (eds.), Proceedings of the 2023 Confer- ence on Empirical Methods in Natural Language Process- ing, EMNLP 2023, Singapore, December 6-10, 2023, pp- 15686-15702. Association for Computational Linguistics, 2023.
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Resolving Lexical Bias in Model Editing
A. Data Construction and Inference for PENME
The projection network is similar to the feed-forward layers transformer as it contains two layers with ReLU activation in between, with the addition of the Batch Normalization layer, a common element in contrastive learning. The network trained via contrastive learning, which requires dataset based on a pair of inputs with positive and negative labels. The algorithm data construction process.
At runtime, upon receiving a user query, PENME checks if the query falls within the editing scope of the edits. If so, the new output from memory is retrieved and the inference process is stopped. Alternativ generation mechanism is discussed in Section of the main paper text, which includes replacing the storage of the new fact as text with playback vectors or LoRa block indices. Additionally, using the retrieved information context learning based generation can be used. The inference pipeline for PENME is given 2.
Algorithm 1 Data Construction for Projector Network
1: Input: num overall negative, threshold edit pairings
2: Input: memory f- O
{Memory storage)
3: Input: dataset.pairs é []
4: Input: :)
(Cosine similarity function]
5: Input: dataset rows r. = (It, Ui, (pit, (pij))
6: for each r; in dataset do
7: for each Pıj and Pij in r; do
8: Add (Ii; Pij) to dataset pairs
(Positive pair}
9: Add (Iz, Piÿ) to dataset_pairs
{Negative pair}
10: end for
for each r, in dataset, where + 1.
if Cos(x;; x4) > threshold edit. pairings then
Add (x) dataset_pait end if
(Negative edit-to-edit pair)
14:
15: for each Přj in do
16: Add (Cos(E,, Prj), (I;, Pij)) to memory
17: end for
18: end for
end for
20: Sort memory in descending order by similarity
21: Add top-num overall negative items from memory to dataset pairs
22: return dataset_pairs
Algorithm 2 Inference for LLM with PENME
1: Input: hx(-)
{LLM output at layer (
2: Input: g(-)
(Projector network]
3: Input: -)..))
{ /Euclidean distance]
4: Input: codebook = (k: (og o }}
5: Input: user prompt st.
6: h4 < -=)(
7: ax (g(h)
8: Find 1s* = arg mink, D(a,, ks)
9: if D(a,, k*) < ug. then
10: return yk
else
return y
(Fallback base LLM output)
13: end if
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Resolving Lexical Bias in Model Editing
B. Paraphrases and Irrelevant prompts Distance Analysis
Table 4 shows the distance between edits and their respective paraphrases and irrelevant prompts across various measurement metrics. From the distances the average paraphrase distance (AvgPD) and average distances between training and test paraphrases (AvgDTTP), we can see that they are generally a little farther than the test paraphrases and are on average bit farther from the edit than train paraphrases. On the other hand, the average irrelevant prompt distance (AvgPN) and average distances between training and test irrelevant prompts (AvgDTTN) show that the test irrelevant prompts are little closer to the edit as compared to the train irrelevant prompts.
C. Comparison Scoping Mechanism: PENME versus MELO and GRACE
To demonstrate the improvement in inference time for selecting the appropriate key, we compare PENME with MELO across various sample sizes of edits, ranging from 50 to 300 in increments of 50 shown in table 5. The results show that PENME outperforms MELO in terms of speed and also highlight the number of keys forgotten during training due to the design of its scoping mechanism, as well as the number of entries for which the radius had to be reduced.
D. Experimentation and Implementation Details
D.1. Experimentation Setup
For our comparative analysis, we contrast against baseline methods such as simple fine-tuning (FT), alongside advanced approaches drawn from relevant literature. These encompass GRACE (Hartvigsen et al., 2023; Yu et al., 2024), employing adapter-based editing with similarity- -based scoping mechanism. SERAC (Mitchell et al., 2022), multimodal editing approach incorporating scoping classifier, memory database, and counterfactual model alongside the target model and MEMIT (Meng et al., 2023) an editing approach designed for decoder only model adopts a model-editing strategy by identifying and updating knowledge-contained model layers' weight matrices.
In evaluating our approach, we adhere to the metrics outlined in $3. Regarding generalization, we define a paraphrase as generalized if it aligns with the correct edit and falls below its distance threshold. For assessing locality, we maintain that locality is preserved when the distance between matched edits exceeds its threshold. Any other instances are categorized as misfires. It is important note that (Hartvigsen al., 2023; Yu et al., 2024) utilize token F1 Accuracy and (Mitchell et al., 2022) use metric based on token probabilities. These metrics are softer in nature which allows for higher scores.
1. COMPUTATION RESOURCES
Training for all projector networks is conducted on an NVIDIA P100 GPU with 16GB VRAM. A larger VRAM or RAM capacity is only necessary for the initial extraction of layer representations from the pre-trained language models. For the evaluation of approaches from relevant literature, some of which demanded greater computational resources, we employed NVIDIA A 100 GPUs with 40GB and 80GB VRAM. All editing approaches were supported are implemented using the default configurations provided in the Easy-Editor library (Wang al., 2023). It is important note that not all models are supported across all editing methods. For instance, Llama-2-7b is not supported for MELO. For some models such as T5-small, limited support is provided therefore, we utilise the code provided by the paper's 's authors.
1.1.2 HYPERPARAMETERS
For training the projector networks, we utilise the Adam optimiser. we experiment with various learning rates 1e1-2 2e2-2 3el-2, 2 we find that a moderate learning rate required to learn faster while not overfitting, hence we choose le" 1-2-2 with learning rate decay rate of 0.01. All projection networks are trained for 200 epochs using a batch size of 8192 and an early stopping patience of 8 epochs. For selecting the margin m in the contrastive learning cost function we ablate on the hyperparameter m for the GPT2-XI. model. The table 6 shows the margin m along with the adjustment to for balanced results for generalization and locality. It can be observed from the table to achieve nigh-performance minimum value of 30 needs to be utilized. The higher the the value for m the better the score for localization. The value chosen is 40 which has the most balanced results.
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Resolving Lexical Bias in Model Editing
Table Distance analysis of distances between edit and its respective paraphrase and irrelevant prompts. The metries for surement include average/max/mmn paraphrase distance (AvpPD average/max/min irrelevant prompts distance MMyDDD,,aaNN/MMiinNN) average/max/min distances between training and paraphrase (AvOOTP)MaxNTTP//MMNDDTTT average distance between farthest edit and closest irrelevant prompt (AvgCPFN), and average/max//min distances between training and test
the
irrelevant prompts (ygDTTTMMaaDDNNNMMiDD
Model
Measurement Metric
Training Set
Test Set
Train vs Test
AvgPD
0.240
0.254
MinPD
0.0
0.02
MaxPD
0.829
1.59
AvgND
1.436
1.379
MinND
0.803
0.616
MaxND
1.884
1.853
Llama-2-7b
AvgCPFN
0.348
0.893
AvgDTTP
0.013
MaxDTTP
1.459
MinDTTP
-0.634
AvgDTTN
-0,227
MaxDTTN
-1.130
MinDTTN
0.0
AvgPD
0.409
0.491
MinPD
0.0
0.002
MaxPD
L.375
1.381
AvgND
0.468
0.534
MinND
0.005
0,010
MaxND
1.384
1.386
T5-small
AvgCPEN
0.193
0,238
AvgDTTP
0.018
MaxDTTP
1.273
MinDTTP
-1.290
AvgDTTN
-0.276
MaxDTTN
-1.341
MinDTTN
0.0
AvgPD
0.378
0.349
MinPD
0.0
0.01
MaxPD
1.49
1.395
AvgND
1.174
1,092
MinND
0.227
0.368
MaxND
1.709
1.728
GPT2-XL
AvgCPEN
0.382
0.700
AvgDTTP
0.008
MaxDTTP
1.368
MinDTTP
-1.046
AvgDTTN
-0.148
MaxDTTN
-0.856
MinDTTN
0.0
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Resolving Lexical Bias in Model Editing
Table Runtime Performance Comparison of PENME MELOO. For PENME, the number of Codebook entries is the same as the
number of edits.
Number of Edits
PENME
MELO/GRACE
Runtime (ms)
Runtime (ms)
Codebook Entries
Edits Forgotten
Edit Conflict
50
0.024 = 0.003
0.316 0.090
269
24
21
100
0,115 0.129
0.364 = 0.050
77
150
0.188 # 0.182
0.624 # 0.082
785
132
1 114
200
0.279 = 0.170
1,423 = 180
1048
188
169
250
0.404 = 0.170
1,681 = 0.205
1319
254
217
0.418 # 0.125
2.149 # 1.069
1554
301
268
Table The table shows how the performance changes along with the required threshold adjustment as margin in contrastive loss changed
Margin
Threshold Adjustment
Generalization
Locality
10
0
0,634
0.831
20
3
0,891
0,880
30
0,958
0,948
40
8
0,967
0.977
50
11
0.978
0.965
60
13
0.976
0,986
70
17
0.973
0.976
80
0.973
0.976
90
20
0.928
0.986
D.2. Data Processing
Counterfact: Each row in the Counterfact consists of edit prompt, two paraphrase prompts, multiple irrelevant prompts and an edit label Zi, Vi. [Pi, Pa), (PTl- For the training dataset, we extract the edit prompt T; one randomly sampled paraphrase P; and half the irrelevant prompts Ptj. For creating additional paraphrases for the training set we utilize the extracted edit prompt and paraphrase prompt as input to ChatGPT and use it to generate three additional paraphrases for training. We ensure that the generated paraphrase follows the (s, r, oo) triplet format that the dataset uses. The test set for locality and generalization compromises of the paraphrase and irrelevant prompts not sampled from the training set.
zsRE: The zsRE dataset comprises of rows containing sample question, its corresponding new label, and multiple rephrased questions along with its filtered rephrased questions. We constructed this dataset following methodologies established in the relevant literature. A balanced subset of paraphrases are derived from the filtered rephrased questions for training and testing purposes. For irrelevant prompts samples, we randomly selected an equal number of questions from the NQ dataset for training and testing while ensuring no overlap in questions.
To highlight the lexicality issue in the datasets, we compute several token overlap metrics between pairs of (edits, paraphrases) (z,, Psy) and (edits, irrelevant prompts) (I,, P.j). The results are presented Table and dataset samples in Table 8. From the token overlap metrics table, it is evident that the edit prompt and irrelevant prompts show high overlap in Counterfact, whereas the overlap is minimal in zsRE This, coupled with the experiment in Section 6.1, highlights the significant challenges observed in the Counterfact dataset.
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Resolving Lexical Bias in Model Editing
Table 7. Comparison between zsRE and Counterfact for token overlap metrics
zsRE
Counterfact
Metric
Pair Type
Score
Precision
Recall
F1
Value
Precision
Recall
F1
Jaceard Similarity
(Ia Psy
0,399
0,401
Jaccard Similarity
[T
0.086
0.430
ROUGE-1
(rt
0.321
0,315
0.316
0.310
0.325
0.307
ROUGE-1
0.076
0.087
0.079
0.295
0.293
0.290
ROUGE-2
(Tt
0.189
0.194
0.194
0.189
0.198
0.184
ROUGE-2
(ra)Pi)]
0.008
0.008
0.008
0,205
0.203
0.201
ROUGE-L
(rt, Pij]
0.299
0,294
0.293
0.299
0.312
0.295
ROUGE-L
(1z;
0.070
0.080
0.073
0.294
0.292
0.289
Counterfact
zsRE
Edit
Paraphrase
Neighbour
Edit
Paraphrase
Neighbour dataset
NQ
The twin city Cologne is
What the twin city of Cologne? It
The twin city of London
Which river system contains Laborec?
What river system does Laborec con- tain?
Where does the name serrano come from?
Alexander Zi- noviev works the area of
Alexander noviev's domain
Zi-
TFred W. Riggs works the area of
Which airport does Air Seychelles op- erate in?
Which airport is closely linked to Air Seychelles?
How many students attend chippewa valley high school? "When do the new sky sports channels launch?
work is
The original lan- guage of Kondura was
The language of Kondura is
The language of Taal is
The country of ori- gin for Kala Pul is what?
Which was the country for Kala Pul?
Thomas Arne died in the city of
Thomas Arne lost their life at
Bill Brandt died in the city of
What label was re- sponsible for Wild World?
What was the label Wild World?
Who composed the music for avengers infinity war?
Table Random samples from the Counterfact and zsRE datasets.
E. Projector Network and Lexical Bias
E.1. Lexical Dominance Layer Analysis
Figure shows the percentage of edits samples where irrelevant prompts were closer the edits for all models across all layers.
E.2. Layer-Wise Analysis of the Projector Network
Figure shows the results for generalization and locality for the T5-small model. The results suggest that performance remains largely consistent; however, training tends require more time to converge higher layers.
F. Visualizations
F.I GENERALIZATION AND LOCALITY for Llama-2-7b
Figure 9 shows generalization and locality trade-off as a function of varying distance thresholds and & for the Llama-2-7b model.
F22 PCA
Figures 10 and 1l present the two-dimensional PCA of the model representations and projector network representations for the Llama-2-7b and GPT2-XL models, respectively. The visual demonstrates that irrelevant prompts are closely aligned with edit prompts, while edit prompts also show proximity to other edit prompts within the original model representations. The projector network, however, effectively mitigates this effect by learning disentangled representation space.
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Resolving Lexical Bias in Model Editing
50
T5-small
GPT2-XL
40
Llama-2-78
Percentage
30
20
10
12
13
14
15
16
17
18
19
20
21
22
23
24
25 26
27
28
44 45
46
47
48
Layer ID
Figure 7. Percentage of samples where edits are closer to irrelevant prompts compared to paraphrases in the representations space of different models across all layers. small, GPT2-XL and Llama-24 2-7b have 6, 32, 48 layers, respectively.
Generalization Locality
0.9
07-
Generalization
Locality
0.5
Model Layer T5-small
Figure 8. Generalization and locality scores for various projector networks trained layers of T5-small using 500 samples from Counterfact.
G. Error Analysis Projector Network
To investigate the reasons behind failures in PENME, we performed comprehensive error analysis across our models. Our findings indicate that contrastive learning significantly mitigates lexical bias. However, due to the inherent variability in lexical pattern distribution within the dataset, there remains potential for further optimization in the projection phase.
The training process of the projector network does not lead uniform distances between each edit, its paraphrases and irrelevant prompts for all samples. This paired with individually varying thresholds for edits leads to misfires. To illustrate this problem, we format the results of each dataset sample for automatic inspection. For all paraphrases and irrelevant prompts in the test set, we extract the nearest key/edit, the ground truth edit/key, the distance to the nearest key/edit, and the distance to the ground truth edit/key. Table shows rouge scores (Lin, 2004) for two possible scenarios i.e. success and failure of generalization and locality. We also show separately distance meeting the threshold. Moreover, since failures can occur in similarities with unrelated edits we show locality and paraphrase failure with both ground truth edit and matched edit.
score for where generalization failure occurs due
For cases of successful generalization, we observe substantial uni-gram overlap and moderate bi-gram overlap between the edited sentences and their paraphrases. The ROUGE-L scores are similarly high for these metrics, indicating that the sentences likely share similar tokens in the same sequence. This implies that the attention mechanism produces similar representations, leading to a high degree of similarity. For locality success, we can see that although there is significant token overlap between irrelevant prompts and their target edits, the irrelevant prompts had higher similarity with some
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Resolving Lexical Bias in Model Editing
Llama-2-70
1,0
Genera Azzeralzzation & Locallty
0.4
0.025
0.050
0.075
0.100
0.125
0.150
0.175
0.200
Threshold r
Cosi ne Sim (Edit to Edit Pairingst
Genera lization
0.7
0.9
-X- Locality
0.8
Figure 9. Generalization and Locality trade-off function varying distance thresholds T
Ø
other edits with low token overlap, this means approach of pushing irrelevant prompt sentences farther away is able to generalize to unseen irrelevant prompts.
In cases of generalization failure, the ROUGE scores for paraphrases compared with the ground truth are slightly lower than those observed successful instances. Although there some token overlap with the target edits, the matched edits exhibit even less token overlap. On the other hand for locality failure, we can see that the prediction case token overlap is higher as compared to locality success, moreover, the overlap is higher compared ground truth edits. Thus lexicality based similarity is not the issue but rather the varying thresholds, which in some cases are large leads misfires.
H. Details Downstream Tasks
For the evaluation of sentiment classification and natural language inference (NLI), we randomly sample 2,000 examples from their respective datasets. For the summarization task, we use 1,000 randomly selected examples. The prompts used for evaluation are listed in Table 10. For both the sentiment classification and summarization tasks, providing a single in-context example necessary achieve reasonable model performance.
I. Details Long Form Generation
Sampled generations using Llama-2-7b with max token length set to 300 are presented in Table 11.
We use the following prompt, which is stored as the value in the codebook:
You have now fact: fodit prompt). Based on this fact, complete the following sentence to answer the question: {query)
Your answer should specifically incorporate the new fact I've shared.
Paragraph: {query)
The generated text shows that the edited fact is propagated throughout the generation, and the generation is coherent.
However, important to note that IKE-based generation, as well as by other approaches, including weight-modifying methods, do not guarantee the elimination ballucinated content that may accompany the edited information. Although
20

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Resolving Lexical Bias in Model Editing
Llama-2-7b
Llama-2-7b Projection Network
0.4
20
0.3
0.2
Neighbours
0.1
Paraphrases
0.0
-0.1
-40
Neighbours Paraphrases
-0.2
-0.3
20
Figure 10. Generalization and locality scores for various projector networks trained on layers of T5-small using 500 samples from Counterfact. The lines show edits and respective paraphrase and irrelevant prompts.
GPT2-XL
GPT2-XL Projection Network
Edits
Neighbours
30
Poraphrases
0.5
0.0
10-
-0.5
10-
-1.0
Edits
Neighbours
-1.5
Parap Paraphrases
20
Figure 11. Two dimensional PCA on GPT2-XL model representation the trained projejctor network.
the target fact is often correctly inserted, unrelated or inaccurate details can still be produced. Addressing this limitation remains an open challenge and promising direction for future work.
For multi-hop editing, prompts whose outputs are expected change can be passed through the model and projector generate corresponding keys. These keys can then be linked central edit, enabling the model runtime retrieve and associate relevant information with the original edit. Through in-context reasoning, the model can subsequently produce the desired output
J. Limitations
Training the projection network in PENME using the contrastive learning scheme is sensitive, requiring tuning of hyper- parameters such as the learning and contrastive loss margin. Effective network training also hinges on the careful construction of training data, which requires consideration of edit-to-edit pairings. Finally, the thresholds for the codebook- based retrieval system, though dynamically determined from training data, can vary across different models, necessitating adjustments to the alpha (a) parameter for each model.
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Resolving Lexical Bias in Model Editing
Table 9. ROUGE Evaluation Scores
Model
Rouge-1 2.5%
Rouge-1 97%
Rouge-2 2 2.5%
Rouge-2 97%
Rougel. 2.5%
Rougel. 97% %
CT
CT
CT
CT
CI
CT
Generalization Success
T5-small
1.00
0.95
1.05
0.706
0.65
0.75
1lama-2-7b
0,629
0.639
0.382
0.394
0.608
0.619
GL
0.655
0.666
0.403
0.417
0.642
0.653
Generalization Failure (prediction)
T3-small
1.00)
0.95
1.05
0,706
0..65
0.75
Llama-2-7b
0.133
0.173
0.056
0.091
0.125
0.162
GPT2-XL
0. 0.122616
0.160
0.056
0.090
0.117
0.153
Generalization Failure (ground truth)
TS-small
1.00
0.95
1.05
0.706
0.65
0.75
Llama-2-7b
0.488
0.518
0.270
0.296
0.460
0.489
GPT2-XL
0.501
0.527
0.284
0.310
0.474
0.500
Locality Success (prediction)
T5-small
0.100
0.104
0.011
0.013
0.096
0.099
Llama-2-7b
0.100
0.104
0.011
0.013
0.096
0.099
GPT2-XL
0.095
0.100
0.011
0.013
0.092
0.095
Locality Success (ground truth)
T5-small
0.100
0.104
0.011
0.013
0.096
0.099
Llama-2-7b
0.487
0.518
0.269
0.296
0.459
0.489
GPT2-XL
0.176
0.217
0.036
0.059
0.173
0.211
Locality Failure (prediction)
T5-small
0.566
0.577
0.390
0.403
0,562
0.574
Llama-2-7b
0.259
0.277
0.148
0.164
0.247
0.264
GPT2-XL
0.254
0,273
0.147
0.164
0.244
0.262
Locality Failure (ground truth)
T5-small
0.203
0.212
0.052
0.058
0.197
0.206
Llama-2-7b
0.201
0,206
0.049
0.053
0.195
0.201
GPT2-XL
0.207
0.218
0,052
0,059
0.201
0.212
Generalization Distance Failure
T5-small
1.00
0.95
1.05
0.706
0.65
0.75
GPT2-XL XL
0.522
0.551
0.279
0.309
0.484
0.512
1llmm-2-7bb
0.495
0.579
0.252
0.324
0.455
0.529
Model
NLI
Sentiment Classification
Summarization
Llama-2-77
[sentencel] } entails the (sentence2). True or False? Answer:
Choose from one these: anger, fear, joy, love, sadness, surprise. Example: (example) Text: (text) Sentiment:
Example: {example) Now generate short summary of the following: Article: (artiele) Summary:
GPT2-XI.
(sentence1) entails the (sentence2).
Choose from of these: anger, fear, joy, love, sadness, surprise. Example: (example) Text: (text)
What is the main takeaway from the following article? (text) Summary:
True or False? Answer:
Sentiment:
Table 10. Prompt used for GPT2-XL and Llama-2-70 for downstream tasks.
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Resolving Lexical Bias in Model Editing
Table 11. Example of an edited factual prompt and generated output.
Prompt Edit
Original
Edit
Query
Output
Information
What is the twin city of Shanghai? It
Barcelona
Dresden
The twin city of Shanghai none other
The twin city of Shanghai is none other than Dresden Germany. This may come as a surprise to many as Dresden is located in Eastern Germany while Shanghai is major city in Eastern China. However, the two cities have a long history cultural and economic ties dating back to the 19th century when both were part of the German Empire. In the years following World War II. Dresden Shanghai maintained close diplomatic relations, and in 1981 the two cities officially designated cach other twin cities. Today, the connection between Dresden and Shanghai continues to flourish with regular exchanges in the fields of education, culture, and trade.
than
Baal Shem of London speaks the language
Hebrew
French
Baal Shem of London proficient in
Baal Shem of London proticient in multiple languages including French. This is remarkable feat given that he is spiritual lcader and healer who said to have the ability communicate with the divine. However his proficiency French particularly noteworthy as it a language that is not commonly associated with his spiritual tradition. Despite this Baal Shem of London has demonstrated remarkable ability to master this language allowing him to connect with and heal individuals from diverse range of cultuaal backgrounds. This yet another testament to his incredible spiritual abilities and his commitment to serving others.
23


Reviews:
[{"review_id": "TEyCYl7nav", "reviewer": "Reviewer_emXV", "summary": "This paper is about parameter preserving knowledge editing methods, specifically adapter based methods for knowledge editing. They show that adapters have lexical bias, which is the vulnerability to recalling unrelated facts due to overlapping n-grams. To mitigate this, it proposes Projector Editor Networks for Model Editing (PENME), which employs contrastive learning to learn a disentangled representation space, effectively separating lexical and semantic similarities.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Comment:\"The only issue was that no downstream performance analysis done.\"**\n\nWe evaluate the general capabilities of the Llama-2-7b used in our study and compare its downstream performance before and after applying PENME. To ensure a diverse and representative assessment, we select three distinct tasks: Natural Language Inference-NLI (RTE dataset) evaluated using F1 score, Summarization (CNN/DailyMail) assessed via average ROUGE-1, ROUGE-2, and ROUGE-L scores, and Sentiment Classification (Diar-AI/Emotions),  using F1 score.\n\n| **Task**                        | **Baseline** | **Edited Model** |\n|--------------------------------|--------------|------------------|\n| NLI | 0.6476       | 0.6428           |\n| Text Classification            | 0.6573       | 0.6573           |\n| Summarization                  | 0.1865       | 0.1865           |\n\nThe results show competitive performance across all tasks, with only a small drop in performance\nin the NLI task.\n\n**Comments:\" How do these edits interfere... generation of every token?**\n\n **In discussing the construction ... didn't explain how to obtain the corresponding values.**\n\n**How are the values incorporated... What is the inference process?**\n\n**How does this work .... generation of every token?\"**\n\nWe apply the projector once after receiving the user input. From that point, the generation process proceeds normally without intervention—unless the similarity between the input and the nearest codebook key falls below a predefined threshold. In such cases, memory retrieval is triggered. As you have pointed out, the values are stored as strings and they are simply appended to the end of the generated sequence. These values are the edits themselves. We did not evaluate generative settings where multi-token or continued generation was necessary. Instead of using a stored string, we could use the approaches examined in GRACE, where a learned vector is the value, and MELO, where a LoRA block is the value.  As a point of emphasis, our proposed scoping mechanism is independent of these design choices, and any generation method can be freely selected.\n\n**Comment:\"The reason why parameter preserving .... evaluations for 2k edits is does no longer enough .... effectiveness for these methods.\"**\n\nWhile AlphaEdit does improve upon MEMIT, it is important to clarify that the “sequential”\nsetting used in AlphaEdit involves 3,000 edits applied in batches of 100, making it more accurately\ndescribed as consecutive batch editing. In contrast, true sequential editing—where updates are applied\none at a time, as explored in GRACE—poses a more challenging scenario due to the cumulative nature\nof parameter shifts (3000 vs 30 updates). We provide results for 3000 sequential edits on the Llama-2-7b model. PENME shows high performance with a minor drop in generalization and locality metrics.\n| **ES** | **Para** | **Loc** |\n|------------------|---------------------------|--------------|\n| 1.00             | 0.8076                    | 0.8549       |\n\n**Comment: \"Consecutive Model Editing ....one of the baselines which came out in March 2024\"**\n\nWe thank the reviewer for highlighting this work. While the initial codebase of the work supports\nGPT series models, adapting it to LLaMA requires additional effort. Notably, Table 1 in the paper shows\nlower generalization and edit success performance on GPT2-XL compared with our method, PENME.\nWe will add the comparison in the final version of the paper.\n\n**Comments:\"How does the key storage ... I dont really know GRACE or MELO?**\n\n**Claim 2: Use contrastive learning .... But the improvement is...**\n**not as clear for CounterFact. ...\"**\n\nGRACE and MELO store direct model representations corresponding to the final hidden token of the input. Their approach relies on maintaining a very low similarity threshold, which necessitates adding a large number of entries to the codebook per edit. To mitigate this overhead, they merge entries with similar outputs. However, when new edits fall within the similarity threshold, the similarity thresholds are reduced for these edits. This threshold adjustment and merging strategy can result in edit forgetting, as discussed in the reference section. \n\nIn contrast, PENME avoids the need to store multiple codebook entries by learning a projection space where irrelevant inputs are mapped farther from each other, while paraphrased edits remain close. This design enables faster retrieval and helps prevent issues like edit clashes and forgetting, without compromising precision. \n\nWe evaluate GRACE by increasing its similarity threshold on GPT2-XL, aiming to match the generalization performance of PENME. The results overall demonstrate how PENME improves upon the direct use of representations.\n\n| Model | ES   | Loc   | Para  |\n|-------|------|-------|-------|\n| PENME | **1.00** | **0.847** | **0.875** |\n| GRACE | **1.00** | 0.171 | 0.767 |", "claims_and_evidence": "Claim 1 :  There is lexical bias in adapter recall\nEvidence : shown through n-gram overlap and Figure 4\n\n\nClaim 2: Use contrastive learning and projector network to disentangle representations of unrelated facts \nEvidence : Shown through editing performance in Table 1. But the improvement is not as clear for CounterFact. Can the authors explain that in more detail?", "methods_and_evaluation_criteria": "They use batch and sequential editing using zsre and counterfact dataset. This is standard in knowledge editing.", "theoretical_claims": "Mostly an empirical study", "experimental_designs_or_analyses": "Experimental design was standard. The only issue was that no downstream performance analysis done. \n\nA question for the authors here is - How do these edits interfere with other task performance and other scenarios like general text generation task? Is the projection mechanism activated at the generation of every token?", "relation_to_broader_scientific_literature": "This paper does present a viable addition to adapter-based knowledge editing methods. \n\nThe reason why parameter preserving methods are attractive is because parameter modifying methods lead to model degradation. But recent knowledge editing methods have made impressive strides in this, preserving downstream performance over 3k sequential edits (AlphaEdit). Performing evaluations for 2k edits is does no longer enough and a good measure of effectiveness for these methods. \n\nSo to show significant addition to the literature would be showing the effectiveness of this method on a much larger set of edits and also measuring downstream performance. Discussing how these methods interact with downstream performance is also important for that.", "essential_references_not_discussed": "Consecutive Model Editing with Batch alongside HooK Layers - Very related method that should have been one of the baselines which came out in March 2024.", "other_strengths_and_weaknesses": "NA", "other_comments_or_suggestions": "NA", "questions_for_authors": "I have the following questions for the authors:\n\n    1. How are the values incorporated since they are stored as string? No presentation of how inference is done? What is the inference process?\n    2. How does the key storage process work for other methods? How is it different? I dont really know GRACE or MELO?\n    3. How does this work with general generation?  Is the projection mechanism activated at the generation of every token?\n\nI am happy to update my score based on the author's responses to the above questions and other questions I asked throughout the review.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nzf2L7ITk2", "reviewer": "Reviewer_Qzbk", "summary": "This paper addresses the challenge of lexical bias in adapter based model editing for large language models LLMs Specifically the authors identify an important limitation in current methods where irrelevant prompts those lexically similar but semantically unrelated to edited prompts are prone to misfires negatively impacting edit locality.\nTo mitigate this the authors propose PENME Projector Editor Networks for Model Editing a framework that introduces a contrastively trained projection network to better disentangle prompt representations. \nBy learning to group semantically similar prompts while distancing lexically similar but unrelated ones PENME improves both locality and generalization in model editing tasks.\nThe method integrates into existing adapter based key value codebooks preserving the efficiency and modularity of these approaches while offering notable improvements in edit precision Experiments on standard benchmarks zsRE and Counterfact across three prominent model architectures T5-small GPT2-XL and Llama-2-7B demonstrate that PENME consistently outperforms state of the art methods such as GRACE and MELO Additionally the authors discuss scalability and codebook management which have been concerns in prior approaches.\n\n## update after rebuttal\nThis paper proposed a method address the lexical bias in model editing task. The response mostly address my concerns, and I keep my original rating.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Comments: The contribution is incremental rather than fundamental it represents an important refinement to adapter based editing but does not propose a fundamentally new editing mechanism**\n\n**The applicability to complex generation tasks for example long form generation reasoning tasks is not explored**\n\n**Q1, How does PENME perform on long form generation tasks where maintaining locality over extended sequences may be challenging?**\n\nWe appreciate the reviewer’s perspective. However, we respectfully argue that our work addresses a fundamental and previously underexplored challenge in the weight-preserving model editing paradigm—namely, the problem of scoping, i.e., determining when an input should invoke the edited knowledge. Our proposed scoping mechanism contributes to the weight-preserving model editing paradigm and is not limited to our editing method, PENME. Moreover, the lexical bias has been highlighted as a substantial issue in text and vision models in the recent paper [1]. Our work is the first work that attempts to address this issue.\n\nWe did not test PENME on long-form generation due to limited available datasets and baselines for meaningful comparison. Long-form generation may require incorporating techniques such as LORA or other lightweight trainable components. We believe that this is out of the scope of the current focus of the paper and is a promising direction for future work. \n\n**Comment: Figure 3 and Figure 7 illustrate the \"Percentage of samples where edits are closer to unrelated neighbors,\" but this is insufficient to demonstrate lexical bias. At lower model layers, high similarity may result from underdeveloped sentence representations, while at higher layers, the reduced percentage indicates greater differentiation between sentences.**\n\n\nWe agree with the reviewer that lower layers of a model may encode less developed sentence representations, while higher layers tend to capture more abstract semantic similarity. However, as shown in the bar chart in Figure 3, lexical bias persists even in the higher layers, suggesting that this issue is not confined to shallow representations. Furthermore, similar findings have been reported by [1], who observed significant lexical bias in representations across a diverse range of vision and text language models. \nThese observations reinforce the broader relevance of the issue we address in this work.\n\n**Comment: Q2, Could the authors comment on PENMEs generalization under domain shift for example specialized domains like healthcare or legal texts?**\n\nWe have evaluated PENME's generalization capabilities by assessing performance transfer from the CounterFact to the zsRE dataset. While our current experiments focus on general QA data, the results suggest that PENME can adapt to new domains with minimal supervision. We hypothesize that for specialized domains such as healthcare or legal texts, with a small amount of in-domain data, PENME would generalize effectively to any domain.\n\n**Comment: Q3, Have the authors considered adversarial training to further enhance the robustness of locality and paraphrase generalization?**\n\nThe CounterFact dataset used in our experiments is inherently adversarial, as irrelevant prompts often share significant lexical and semantic overlap (e.g. the subjects are both actors) with the edited facts. Additional adversarial examples could indeed be constructed using larger language models to further enhance robustness and generalization. However, generating such data at scale would increase computational cost and resource requirements for training. This is a valuable future direction that leads the way towards PENME generalization to a wide set of domains.\n\n**Comment: Q4, How might PENMEs projection space handle multi hop reasoning prompts where lexical and semantic relationships are more complex?**\n\nWe posit that a knowledge base will be needed, and such prompts would need to be stored in the codebook with links to the main edit. PENME's learned representation space would allow it to localize this information better in the representation space, thus allowing for scoping multi-hop or one-hop questions. At inference time, these can be used to link back to the edited information, which can then be used for informed generation through some training of additional parameters, e.g. LoRA block. \n\n\n[1] Dumpala, S. H., Jaiswal, A., Sastry, C., Milios, E., Oore,\nS., and Sajjad, H. SUGARCREPE++ Dataset: Vision Language Model Sensitivity to Semantic and Lexical Alterations. In Conference on Neural Information Processing Systems, Dataset Track (NeurIPS), 2024.", "claims_and_evidence": "The paper makes the following key claims:\n\n1, Lexical dominance exists in existing adapter based editing systems and leads to locality degradation\n\n2, PENME reduces lexical bias thereby improving both locality and paraphrase generalization\n\n3, PENME outperforms prior methods in standard evaluation metrics on zsRE and Counterfact datasets\n\n4, The framework is scalable requiring fewer codebook entries and reduced management overhead\n\nIn my assessment these claims are largely substantiated\n* The authors present convincing empirical evidence particularly the layer wise lexical dominance analysis Figure 4 which demonstrates the motivation behind their approach\n* Quantitative results in Table 1 reflect consistent improvements across multiple evaluation metrics\n* The ablation studies and scalability analyses Figures 5 and 6 Section 4 offer further support for the systems design decisions\n* The evidence is thorough and the experimental results align well with the claims made", "methods_and_evaluation_criteria": "The methodology is appropriate and thoughtfully designed Leveraging contrastive learning to train the projection network is a natural choice for encouraging representation disentanglement. The projection network is lightweight a simple two layer MLP which ensures efficiency\n\nThe evaluation metrics edit success locality and paraphrase generalization are standard in model editing research and appropriate for this work. The datasets selected zsRE Counterfact are commonly used and offer a reasonable degree of diversity in task difficulty.", "theoretical_claims": "This paper does not make formal theoretical claims that require rigorous proof verification. However the conceptual grounding of PENME is sound and well supported by empirical evidence.", "experimental_designs_or_analyses": "The experimental design is robust\n* Multiple LLM architectures are considered increasing the generality of the findings\n* The use of both batch editing and streaming edit scenarios demonstrates flexibility\n* The ablation studies on key hyperparameters τ ϕ are comprehensive and provide insight into the tradeoffs inherent in the approach\n* Results are presented clearly with appropriate baselines GRACE MELO SERAC MEMIT included for comparison\nMy suggestion here would be to extend the evaluation to additional datasets perhaps outside of fact based knowledge editing to explore how well PENME generalizes.", "relation_to_broader_scientific_literature": "The paper builds directly on prior adapter based editing methods GRACE, MELO while drawing from contrastive learning principles common in representation learning.", "essential_references_not_discussed": "N.A.", "other_strengths_and_weaknesses": "Strengths:\n- The problem of lexical bias in model editing is underexplored and the authors offer a practical well motivated solution\nPENME demonstrates clear empirical improvements over strong baselines\n- The method scales well and reduces codebook management complexity which addresses a common criticism of adapter based approaches\n - The paper is well written and accessible even for readers not deeply familiar with model editing.\n\nWeaknesses:\n- The contribution is incremental rather than fundamental it represents an important refinement to adapter based editing but does not propose a fundamentally new editing mechanism\n- The applicability to complex generation tasks for example long form generation reasoning tasks is not explored", "other_comments_or_suggestions": "* Figures 1 and 4 could be made clearer axis labels and figure legends are somewhat difficult to read\n* The authors may wish to include qualitative examples of edits for example paraphrases handled correctly to better illustrate practical gains\n* There is an opportunity to discuss potential defensive mechanisms to mitigate malicious editing", "questions_for_authors": "Q1, How does PENME perform on long form generation tasks where maintaining locality over extended sequences may be challenging?\n\nQ2, Could the authors comment on PENMEs generalization under domain shift for example specialized domains like healthcare or legal texts?\n\nQ3, Have the authors considered adversarial training to further enhance the robustness of locality and paraphrase generalization?\n\nQ4, How might PENMEs projection space handle multi hop reasoning prompts where lexical and semantic relationships are more complex?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LQ9GkCkBTe", "reviewer": "Reviewer_jhu1", "summary": "This paper proposes PENME a learnable projection network to transform the model’s internal representation such that lexical bias during model tuning is minimized. In particular, the authors found that existing adapters often misfire on inputs that share words with a stored edit but aren’t actually the same fact, while also failing to generalize to paraphrases. To address this PENME attempts to minimized & pull semantic paraphrases closer while lexically similar prompts are pushed farther apart. Additionally, PENME shows strong empirical results on several benchmarks with significantly better generalization abilities and computationally efficiency.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Comment: The paper establishes the issue of lexical bias in model editing which provides strong insights. Additionally the PENME framework is a intuitive next step. However, this leads PENME's contributions to be less significant methodologically.** \n\nWhile we understand that PENME may appear as a natural extension, we respectfully argue that our work addresses a fundamental and previously underexplored challenge in the weight-preserving model editing paradigm—namely, the problem of scoping, i.e., determining when an input should invoke the edited knowledge. Our proposed scoping mechanism contributes to the weight-preserving model editing paradigm and is not limited to our editing method, PENME. Moreover, the lexical bias has been highlighted as a substantial issue in text and vision models in the recent paper [1]. Our work is the first work that attempts to address this issue.\n\n[1] Dumpala, S. H., Jaiswal, A., Sastry, C., Milios, E., Oore,\nS., and Sajjad, H. SUGARCREPE++ Dataset: Vision Language Model Sensitivity to Semantic and Lexical Alterations. In Conference on Neural Information Processing Systems, Dataset Track (NeurIPS), 2024.", "claims_and_evidence": "The paper's claims are clear with some experimental results to backup the idea of PENME having general improvements over prior methods.", "methods_and_evaluation_criteria": "PENME as a solution to the given problem is well motivated and the idea of inserting a learned projection module to re-map the latent space for editing is intuitive. Additionally, the chosen counterfactual and zsRE datasets are standard benchmarks from prior works. Overall, the reviewer has no high level concern with the proposed methodology and experimental design.", "theoretical_claims": "The paper has no direct theoretical claims and therefore rigorous proofs are not relevant.", "experimental_designs_or_analyses": "The chosen benchmarks are fair and follow in line with prior works. Additionally, the set of comparative methods is broad alongside a range of model architectures. Finally there are also a large amount of ablation studies which cover a series of supporting analysis.", "relation_to_broader_scientific_literature": "The paper positions itself well in the context of prior model editing research. The observations of lexical bias is also an under explored area of research and would help broaden the scope of the model editing.", "essential_references_not_discussed": "The reviewer is unaware of other necessary references which are not linked.", "other_strengths_and_weaknesses": "The paper establishes the issue of lexical bias in model editing which provides strong insights. Additionally the PENME framework is a intuitive next step. However, this leads PENME's contributions to be less significant methodologically.", "other_comments_or_suggestions": "See the sections above.", "questions_for_authors": "No additional questions beyond the issues raised in the sections above.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UFeKT58uOh", "reviewer": "Reviewer_cuog", "summary": "This paper propose a method for model editing, following GRACE paper. The authors witnessed that the model intermediate representations are ambiguous to distinguish irrelevant prompts and paraphrases given an editing prompt. The authors adopt the contrastive learning technique to well separate them, thus enforcing better locality and generalizability.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Comments: There is no definition given for Lexical Bias in the paper.**\n\n**The evidence presentation is a bit messed up. For example,\nFigure 2 uses \"Irrelevant Prompts\" while Figure 3 uses \"Neighbors\". ....... should be Llama instead of LAMA**\n\n**There are inconsistencies between figures. For example,\nFigure 1 and Figure 4 shows the same evaluation,... than gpt2 in Figure 1 while has smaller percentage in Figure 4.**\n\nWe thank the reviewer for pointing out the typographical error; neighbours and irrelevant prompts refer to the same examples. This will be corrected in the final version. We will improve the images so that the bars are separated, we provide colour correction for Figure 5: (https://imgur.com/a/bFPCBEB). \n\nRegarding figures 1 and 4, the experiments were conducted using different random splits, which accounts for the observed discrepancies in the reported numbers. We will update the figures to ensure consistency in the reported percentages.\n\nWe define lexical bias as prompts with similar lexical tokens but different semantics that are closer together in the representation space as compared to a prompt and its respective paraphrases.\n\n**Comment: For locality, do you compute the train retain rate or the test retain rate (c.f. GRACE paper)?**\n\nWe compute the locality metric on the test set same as GRACE.\n\n**Comment: It's not clear how the codebook ..... LORA indices, how to implement this?**\n\nWe apply the projector once after receiving the user input. From that point, the generation process proceeds normally without intervention—unless the similarity between the input and the nearest codebook key falls below a predefined threshold. In such cases, memory retrieval is triggered. As you have pointed out, the values are stored as strings and they are simply appended to the end of the generated sequence. These values are the edits themselves. Learned vectors can be used to steer the model's generation by adding them to the token representations after identifying the edit scope. Similarly, LoRA blocks can be trained per edit to modulate generation in response to each edit. \n\n**Comment: It's not clear which token embedding is used for training the projection network. The authors only mentioned that the projection network is applied to only one certain layer, but it's not clear the embedding of which token in the prompts / answers is used for training.**\n\nWe use averaged token representations for input encoding. While representations of the final token can also be used to train PENME, they tend to be heavily biased, often inflating similarity between prompts that share the same ending token.\n\n**Comments: Many typos / errors in the methodology section. For example,\nequation 2, .........how to compute the euclidean distance between two strings?**\n\nWe apologize for the typographical errors. In Equation (2), the argmin operation applies only to the keys $k_i$. The variables $\\vec{x_i}$ and $\\vec{p_{ij}}$ refer to the output representations from the projection network, which these implied through text rather than a formal definition. We will revise the text to clarify this and eliminate any confusion.\n\n**Comments: The 4.3 title is misleading. There is no learning process to determine the threshold and tau. tau is a hyperparameter and the threshold depends on the paraphrases chosen.**\n\nWe will replace \"Learning\" from the title with   \"Finding\" to precisely reflect the content of the section.\n\n**Comment: Have the authors tried other contrastive learning losses ...thus hurt the locality, which is shown in Table 1 for Counterfact Loc metric.**\n\nThe balance is implied in terms of average score for generalization and locality in comparison with other approaches, where there is a significant deterioration of one metric as compared to the other. We initially evaluated several contrastive learning approaches and found that none consistently outperformed our formulation. This result aligns with the findings of [1], which demonstrate that, in general, the performance of various metric learning methods tends to be comparable.\n\n**Comment: In addition, MEMIT as a baseline ... include this one as a baseline.**\n\nMEMIT is a weight modifying approach which improves over ROME with adjustments to model parameters for all layers instead of one. It is a high performing baseline for weight modifying approaches. We will add the citation alongside ROME for the final version. Currently, the work is cited in experimental setup details provided in Appendix D.1. Experimentation Setup.\n\n**Comment: Why the Para metric is a bit low on the easier zsRE dataset?**\n\nThis is for zero-shot transfer from CounterFact to zsRE where some drop in performance is expected. Despite no training on zsRE, PENME still outperforms most methods.\n\n[1]Musgrave, Kevin, Serge Belongie, and Ser-Nam Lim. \"A metric learning reality check.\" In Computer Vision–ECCV 2020: 16th European Conference\"", "claims_and_evidence": "There is no definition given for Lexical Bias in the paper.\n\nThe evidence presentation is a bit messed up. For example,\n\n1. Figure 2 uses \"Irrelevant Prompts\" while Figure 3 uses \"Neighbors\". There is no definition for \"neighbors\" throughout the paper\n2. The bars overlap with each other in Figure 4\n3. The legends for color do not match those lines in Figure 5. There is no green line or clear blue line\n4. The legends for Figure 6 should be Llama instead of LAMA\n\nThere are inconsistencies between figures. For example,\n\n1. Figure 1 and Figure 4 shows the same evaluation, i.e., the percentage of samples where irrelevant prompts are closer to the edits compared to semantically similar prompts. However, For T5small, Figure 1 shows 46.8% while Figure 4 shows more than 60% in layer 2. Llama has bigger percentage than gpt2 in Figure 1 while has smaller percentage in Figure 4.", "methods_and_evaluation_criteria": "1. For locality, do you compute the train retain rate or the test retain rate (c.f. GRACE paper)?\n2. It's not clear how the codebook is used in the experiments. If the stored values are strings as mentioned in the paper, then for edited prompts, there is no need to go through the full autoregressive generation process, but directly output the stored value in the codebook. The authors also mentioned that the stored values can be vectors or LORA indices, how to implement this?\n3. It's not clear which token embedding is used for training the projection network. The authors only mentioned that the projection network is applied to only one certain layer, but it's not clear the embedding of which token in the prompts / answers is used for training.\n4. Many typos / errors in the methodology section. For example, \n+ equation 2, why argmin has underscore value v_i?\n+ Threshold in line before equation 2 is v_i^delta, while it's v_delta^i in equation 2.\n+ In equation 3, x_i and p_ij are both strings according to definition in Section 3, what is a string vector (with an arrow on top)? how to compute the euclidean distance between two strings?\n5. The 4.3 title is misleading. There is no learning process to determine the threshold and tau. tau is a hyperparameter and the threshold depends on the paraphrases chosen.\n6. How to choose which layer for training?\n7. Have the authors tried other contrastive learning losses, e.g., InfoNCE, etc.?\n8. In line 223, why the formulation 3 allows the method to achieve an optimal balance between generalization and locality preservation? How is this optimal? It seems that this formulation can only help generalizability since it preserve the most far away paraphrase which may also include irrelevant edits, thus hurt the locality, which is shown in Table 1 for Counterfact Loc metric.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "Please refer to other sections.\n\nIn addition, MEMIT as a baseline is not discussed in the paper, e.g., not in the Related Work section. It's not clear why the authors include this one as a baseline.", "relation_to_broader_scientific_literature": "Model editing is a trending topic in LLM research. There are many previous works. Many papers have shifted from batch editing to lifelong / continual editing. In this paper, the authors also show results in this setting. Why the Para metric is a bit low on the easier zsRE dataset?", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "Please refine the writing and the presentation of the paper.", "questions_for_authors": "Please refer to other sections.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XLiPp9OOJv", "reviewer": "Reviewer_s3vv", "summary": "This paper addresses the challenge of editing the outputs of large language models without degrading their overall performance. Traditional methods directly modify model weights, often causing undesirable side effects. In contrast, recent approaches use adapters that trigger edits based on semantic similarity in the representation space. However, these adapter methods are shown to be susceptible to strong lexical biases—resulting in unintended edits for prompts with overlapping words. To overcome this, the paper introduces a principled method for learning a disentangled representation space. This new space allows for the precise localization of edits by keeping unrelated prompts distant while keeping semantically equivalent (or paraphrased) prompts close together. The proposed method, Projector Editor Networks for Model Editing (PENME), achieves state-of-the-art editing performance. It is not only more computationally efficient during inference than previous approaches but also adaptable across various architectures.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Comment: Although adaptable across architectures, the paper may not fully explore how the approach scales with increasingly larger and more complex models**\n\nThe proposed approach operates at a single layer of the model, making it lightweight and efficient, with minimal dependence on model size. Scalability is not a major concern, as the method avoids full model retraining and extensive parameter updates. In our experiments, we employed a direct output strategy by modifying the architecture to support multi-output generation. However, generation control mechanisms such as playback vectors and lightweight LoRA blocks have also demonstrated robust performance and can serve as effective alternatives to direct output. These design choices suggest that the approach can scale well to larger models or more complex generation tasks without incurring significant computational overhead.\n\n**Comment: The success of precise edit localization hinges on the quality of the learned disentangled representation, which might require extensive tuning and may not generalize perfectly across all domains.**\n\nOur analysis identifies the key factor contributing to locality challenges in the model’s representation space, which is lexical bias.  We have evaluated PENME’s generalization capabilities by assessing performance transfer from the CounterFact to the zsRE dataset. While our current experiments focus on general QA data, the results suggest that PENME can adapt to new domains with minimal supervision. We hypothesize that for specialized domains, PENME can generalize with a small amount of in-domain data.", "claims_and_evidence": "The claims made  in the submission are supported by the experiments.", "methods_and_evaluation_criteria": "The proposed methods make sense for the problem.", "theoretical_claims": "This paper does not contain theoretical claims.", "experimental_designs_or_analyses": "The experimental designs are sound.", "relation_to_broader_scientific_literature": "This paper demonstrates that current adapter methods are critically vulnerable to strong lexical biases, which can result in edits being applied to irrelevant prompts that share overlapping words. This paper introduces a principled approach to learning a disentangled representation space, enabling the precise localization of edits by keeping unrelated prompts distinct while preserving the proximity of semantically similar paraphrases.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths:\n\nBy learning a disentangled representation space, the method effectively differentiates between relevant and irrelevant prompts, reducing unintended modifications.\n\nPENME offers faster inference times compared to prior methods, making it more practical for real-world applications.\n\n\nThe paper identifies and mitigates the vulnerability of previous adapter methods to strong lexical biases, enhancing reliability. The approach is designed to work across different model architectures, increasing its applicability.\n\nWeaknesses:\n\nAlthough adaptable across architectures, the paper may not fully explore how the approach scales with increasingly larger and more complex models.\n\nThe success of precise edit localization hinges on the quality of the learned disentangled representation, which might require extensive tuning and may not generalize perfectly across all domains.", "other_comments_or_suggestions": "No.", "questions_for_authors": "See weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces a new approach for model editing, focusing on a new scoping mechanism for weight-preserving editors to decide when edits should be applied. This helps mitigate the flaw they identify with prior works: Edits are often falsely triggered for inputs that have n-grams that are present in prior edits, even when they are semantically distinct. Their approach learns to project edits into a new space in which their semantic differences are emphasized, leading to better localization while maintaining generalization of the edits.

Reviewers overall lean positively, though there are some concerns. On the positive side, reviewers appreciated the novelty of the solution, the identified weakness of prior works, sufficient experimental comparisons, and the outperformance of alternatives. On the negative side, reviewers are concerned by a lack of downstream evaluations (which is a big, recent focus of model editing works) and that the practical implications may be limited since model edits are only for QA tasks. They also note some opportunities to further clarify the writing (e.g., "lexical bias" is in the title, but isn't clearly defined in the paper).

Overall, the experiments appear sound and that advances beyond the state-of-the-art are compelling, so I believe the strengths outweigh the weaknesses and suggest acceptance.
Decision: Accept (poster)