Hippocampal-like Sequential Editing for Continual Knowledge Updates in Large Language Models
Authors: Quntian Fang
Zhen Huang
Zhiliang Tian
Minghao Hu
Dongsheng Li
Yiping Yao
Xinyue Fang
Menglong Lu
Guotong Geng
Venue: NeurIPS 2025 poster ()
Keywords: Large Language Models
Hippocampal Mechanisms
Machine Unlearning
Catastrophic Forgetting
Hallucination Mitigation
Long-term Editing Memory
Primary area: deep_learning
TL;DR: The HSE algorithm inspired by hippocampal mechanisms, enables sequential editing of large language models, striking a balance between learning new knowledge and preventing catastrophic forgetting.

Abstract: Large language models (LLMs) are now pivotal in real-world applications. Model editing has emerged as a promising paradigm for efficiently modifying LLMs without full retraining. However, current editing approaches face significant limitations due to parameter drift, which stems from inconsistencies between newly edited knowledge and the model's existing knowledge. In sequential editing scenarios, cumulative drifts progressively lead to model collapse characterized by general capability degradation and balance between acquiring new knowledge and catastrophic forgetting of existing knowledge. Drawing inspiration from the hippocampal trisynaptic circuit for continual memorizing and forgetting, we propose a Hippocampal-like Sequential Editing (HSE) framework that designs the unlearning of obsolete knowledge, domain-specific knowledge update separation and replay for edited knowledge. Specifically, the HSE framework designs three core mechanisms: (1) Machine unlearning selectively erases outdated knowledge to facilitate integration of new information, (2) Fisher Information Matrix-guided parameter updates prevents cross-domain knowledge interference, and (3) Parameter replay consolidates long-term editing memory through lightweight and global replay of editing data in a parametric form. Theoretical analysis demonstrates that HSE achieves smaller generalization error bounds, more stable convergence and higher computational efficiency. Experimental results validate its effective balance between acquiring new knowledge and mitigating catastrophic forgetting, maintaining or even slightly enhancing general capabilities. In practical applications, experiments confirm its effectiveness in multi-domain hallucination mitigation, healthcare knowledge injecting, and societal bias reduction.

Hippocampal-like Sequential Editing for Continual Knowledge Updates in Large Language Models
Quntian Fang
Zhen Huang Zhiliang Tian Minghao Hu
Dongsheng Li'
Yiping Yao²
Xinyue Fang' Menglong Lu Guotong Geng
National Key Laboratory of Parallel and Distributed Computing "College of System Engineering
Keyy Laboratory of Advanced Microprocessor Chips and Systems
National University of Defense Technology
*Center of Information Research, AMS hwwgguggheanaaggeeeeneee
(fangquntian, huangzhen, ti tiannhiiiamm fangxinyue, lumenglong) @nudt edu. 0163. com
(humh573, (ar.de77,ggoonlnn 01633
Abstract
Large language models (LLMs) are now pivotal in real-world applications. Model editing has emerged promising paradigm for efficiently modifying LLMs without full retraining. However, current editing approaches face significant limi- tations due parameter drift, which stems from inconsistencies between newly edited knowledge and the model's 's existing knowledge. In sequential editing sce- narios, cumulative drifts progressively lead to model collapse characterized by general capability degradation and balance between acquiring new knowledge and catastrophic forgetting of existing knowledge. Drawing inspiration from the hippocampal trisynaptic circuit for continual memorizing and forgetting, we pro- pose Hippocampal-like Sequential Editing (HSE) framework that designs the unlearning of obsolete knowledge, domain-specifee knowledge update separation and replay for edited knowledge. Specificallyy the HSE framework designs three core mechanisms: (1) Machine unlearning selectively erases outdated knowledge to facilitate integration of new information, (2) Fisher information matrix- matrix-guided parameter updates prevents cross-doman knowledge interference, and (3) Parame- ter replay consolidates long-term editing memory through lightweight and global replay of editing data in a parametric form. Theoretical analysis demonstrates that HSE achieves smaller generalization error bounds, more stable convergence and higher computational efficiency. Experimental results validate its effective balance between acquiring new knowledge and mitigating catastrophic forgetting, main- taining even slightly enhancing general capabilities. In practical applications, experiments confirm its effectiveness in multi-domain hallucination mitigation, healthcare knowledge injecting, and societal bias reduction. Our code available at HSE_
Introduction
In real-world applications, large language models require frequent updates to correct erroneous Nevertheless, directly retraining LLMs incurs substantial
or outdated knowledge [I
38)
"contributed equally to this work.
i "Correppondigg authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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computational costs. Consequently, model editing has emerged as paradigm to precisely modify LLMs' behavior for specific knowledge, while preserving performance on unrelated knowledge [
For practical deployment [49 36). L.LMs must integrate cutting-edge research while simultanoously discarding outdated knowledge and preserving validated knowledge. Therefore, sequential editing of LLMs further elevates the model editing approach to continual learning paradigm, aiming to ensure that LLMs retain all edited knowledge across multiple editing operations and preserve the general capabilities [65] 67 335. Mainstream model editing methods focus on learning from static datasets, whereas they are less effective in handling sequential data streams [65] 33]. This poses significant challenges, particularly in dynamic environments where models need to continuously update and adapt to new knowledge.
65
67
To address the challenges of sequential editing, researchers have proposed two main approaches. (I) parameter-preservinn approach introduces additional modules integrated into L.MMs, incorporating extra trainable parameters 22 [32 or network memory components (20 to record past edits while freezing the original model parameters. Nonetheless, this method tends to conservatively preserve the integrity of the original model parameters, which may limit its ability to adequately capture evolving data distributions. (2) Parameter-modiffyngg approach focuses on modifying the model parameters directly to adapt to new knowledge. This approach first identifies the relevant parameters associated with the new knowledge and then computes an update matrix to modify [4] [4]. In comparison, the parameter- monerttrrrmmiiyiinggg approach enhances adaptation to new tasks by updating only the parameters directly associated with the task, leading to a more lightweight update process and superior generalization performance [65] [O]. Nevertheless, the parameter-modiin modiifyiin method may introduce conflicts between the I.MMs' existing knowledge and the external editing knowledge (34). In sequential editing scenarios, these conflicts can accumulate, leading to parameter drift in the edited layers of LLMs, making them incompatible with other parameter layers [15]. The accumulation of such drifts can subsequently lead model collapse. Therefore, under sequential editing settings, the LLMs are susceptible to several issues: degradation of performance on general capabilities, and a delicate balance between acquiring new knowledge and catastrophic forgetting of existing knowledge. These issues make it difficult to apply existing model editing methods practically in sequential editing.
Fortunately, these issues effectively addressed in biological systems, which exhibit continual learning and adaptation throughout their lifetimes [59] [8] 66]. The hippocampus, a key brain struc- ture, possesses effective mechanisms for continual memorizing and forgetting [55] 51]. Synaptic plasticity in the hippocampus modulates neuronal activity by regulating synaptic strength, thereby facilitating both memorizing and forgetting [51 2 19] During the processes of memory encoding, the hippocampus employs pattern separation to effectively distinguish between different input pat- terns 66] 17 42), thereby enhancing the stability of memories. The replay mechanism that occurs during rest periods supports the transformation from short-term to long-term memory within the hippocampus 266 52) This process involves the reactivation of neural traces of previously experi- enced events, which significantly strengthen memory consolidation. Thus, the biological mechanisms within the hippocampus are essential for continual memorizing and forgetting processes, serving as an inspiration for the development of sequential editing.
In this paper, we propose Hippocampal-like Sequential Editing (HSE) method, which designs the unlearning of obsolete knowledge, domain-specifie knowledge update separation and replay for edited knowledge. HSE designs the following editing strategies inspired by the biological mechanisms
observed in the part of hippocampal trisynaptic circuit (DG->C33-CAI [3 [5) (1) To address the
confficts between existing knowledge and editing knowledge, HSE adopts memory-directed active forgetting strategy for machine unlearning to discard knowledge within LLMs that is inconsistent with the edits, inspired by how the hippocampus employs long-term depression (LTD) in CA3-CAI to selectively forget outdated information (2]. (2) Given that new knowledge may originate from diverse domains, HSE utilizes weight matrix (Fisher information matrix 48 29 to determine the importance of each parameter for the editing knowledge from different domains and appropriately controls update magnitude during sequential editing. This comain-separaan mechanism is inspired by the way that dentate gyrus (DG) in hippocampus employs pattern separation mechanism to distinguish knowledge from different domains, thereby reducing interference between them [42). This facilitates LLMs edit knowledge from diverse domains while significantly reducing mutual interference. (3) To balance between acquiring new knowledge and catastrophic forgetting of existing knowledge, our proposed HSE derives closed-form solution to promote the long-tent term editing memory of the LLMs, inspired by that hippocampus employs memory consolidation in the CA3 and CAI region (26).
2

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(4) Trisynapdic Circuit in the Hippocampus
(b) Our proposed method HSE
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Figure Illustration of the HSE method inspired by the hippocampal trisynaptic circuit. where ① LTP and LTD
(a) The trisynaptic circuit within the hippocampus occurring between CA3 and CAI are responsible for mechanisms of memory formation and forgetting, respectively. @ The DG handles pattern separation and @ CA3 facilitates sharp-wave ripples (SWRs) in CA1 to consolidate memories. (h) Our proposed method HSE is inspired by biological mechanisms, including ( active forgetting based on machine unlearning. control of model editing parameter updates using the Fisher information matrix (FIM), and @ long-term editing memory that reinforces edited knowledge and prevents parameter surge. vy denote the Jeh. dimension of input and output for the ieh. editing knowledge, respectively.
Building on this biological mechanism, HSE progressively replays long- term editing memories to ensure the sustainability and convergence of the editing process.
Our experimental results show that HSE significantly outperforms existing model editing methods across multiple benchmarks. Compared to the best baseline, our approach demonstrates average improvements of 20.6% generalization, 21.9% in specificity and 17.3% in efficacy. In contrast to other editing methods, which suffer a catastrophic drop in performance to zero, our approach not only preserves but also even improves the general capabilities of the original LLMs after sequential editing on ZsRE question answering datasets. Theoretical analysis confirms that the HSE method exhibits tighter generalization error bounds, more stable convergence and higher computational efficiency. Furthermore, HSE facilitates significant practical advancements in mitigating multi- domain hallucinations. updating healthcare knowledge and reducing societal biases for LLMs.
2 Methods
2.1 Task Formulation
For the is editing operation on a LLM f. given the ich new knowledge (subject s; relation rı, object of) with its corresponding old knowledge (si, ri, oi), sequen- tial editing task of LLM aims to integrate new editing knowledge while preserving previous edited knowledge and maintaining general abilities. The ish editing operation on results f.. The editing data is defined as e; = (st, ri. ot, o,), and the editing operation is formulated as f++1 = E, e.). After applying a sequence of editing operations E {E..-. En the LLM evolves into fn. For all editing data {e: = (8i, ri, 0i, of ]i S n) the LLM should generate the edited target objects fa (.)
r:) = or- For data points H not associated with the editing data, the LLM should retain the original predictions, ensuring that f. ~ fo) where fo denotes the initial LLM.
The second linear layer W of the feed-forward network (FFN) in the early transformer modules is often regarded key-value associative memory 44 43 12 This memory encodes the editing knowledge to ensure that the encoded input are accurately mapped to the target outputs. Due the significant semantic information carried by the last subject token 4.), the input encoding of the last subject token before entering edited linear parameter W is denoted as k E R2, and the output encoding the updated object o in W is denoted as v E gd For series of key-value knowledge pairs K = {k1, ... ..,, and V = {v...... Ux J. the LLM stores this series of knowledge if the condition W K V is satisfied. Let knowledge triple (st, 0,) be the ith editing knowledge.
3

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{z,10 S {) be the random token prefixes, Enow (-) be the input encoding of the last subject
token to W. The encoding forms of lt and v as follows.
P
k; P
Enew (xy @ 0%),
(1)
Wk; + 6,
(2)
Here, 6, serves the learnable incremental update applied the original output of W. referred to as the "incremental update parameter". addition enable the model to remember the edited knowledge, it is also necessary to preserve the previously stored knowledge pair (Ko, Vo). We randomly sample triples from Wikipedia to encode Ko and obtain Vi = W Ko [44]. Therefore, after computing K and V. update matrix A is introduced to modify the original parameters W in order to satisfy the desired mapping relationship as follows:
(W A) = V, + A)Ko Vo
(3)
By applying the normal equations
53
Chapter 4.3], the close-form solution of Eq. 3 is:
(KoK" KKT)-
(4)
2.2 Overview
Our sequential editing method HSE designs several hippocampalllike mechanisms to precisely modify the model's parameters. As shown in Fig. T these mechanisms support memory-directer active forgetting, memory stability preservation to optimize the encoding of editing knowledge, and also progressive memory consolidation to replay existing edited knowledge.
(1) Memory-directed Active Forgetting (Sec 2.3 leverages machine unlearning to actively forget old knowledge within the model that inconsistent with editing knowledge. (2) Memory Stability Preservation (Sec(..4) utilizes the Fisher information matrix to control the updates of model parame- ters, avoiding mutual interference between editing data from different domains to preserve model stability. (3) Progressive Memory Consolidation (Sec (.5c achieves long-term memory in the model through the progressive replay of edited knowledge using long-term editing memory.
2.3 Memory-directed Active Forgetting via Machine Unlearning
To address the conflicting between new editing knowledge and existing knowledge LLMs have learned [34], we propose leverage machine unlearning to achieve active forgetting of outdated knowledge. The conflict between new and old knowloedge occurs when LLMs edit the data that inconsistent with the internal knowledge of LLMs. The memory processes in the hippocampus indicate that both memorizing and active forgetting are key mechanisms for the brain to function efficiently 3 4 However, current model editing efforts primarily focus on enhancing the memorizing ability [35), with less attention paid to active forgetting. Fortunately, by incorporating machine unlearning techniques, the LLMS can selectively forget outdated knowledge while efficiently acquiring and integrating new knowledge, thereby enhancing its adaptability and performance in dynamic environments.
Specifically, for the editing data (xr, y) E D and corresponding data points (r. 77) E H that need be forgotten, we model the forgetting objective using ps (ū(x) )r) to appropriately reduce the probability of retaining old knowledge within the LLMs. We first construct the maximum likelihood estimation (MLE) optimization formulate the memorizing and active forgetting objectives for the incremental update parameter 8 in Eq. 2 as shown in Eq. 3 where a balances the degree of memory and forgetting.
8 = arg minminn-2
Σ (1 a)
Σ log - ())))
(5)
(R.N)ED
(=.j)e
editing
machiie unlearning
Subsequently, we employ the unlikelihood loss 63 to achieve the objective in Eq. 5 loss of 0g is (0)r((0)). The cross-entropy loss and the unlikelihood loss is denoted as Lcv and Lu (0s) respectively. The LMAF (0s) can be defined the negative of Eq.
We define the

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LMAF(8) (o + Loz(0) = D
a
Σ (=.))D
log Po (y)2)
(1 - a) D
Σ (x.
log [1 [=(]]]] (6)
When the optimizer Adam, we theoretically explain that the unlikelihood loss leads to a reduction in the generalization error bound. This indicates that our method, after editing LLMs, can effectively adapt to more generalized scenarios for editing tasks.
Following the generalization error bounds theorem established in the work 31 Theorem 5.5], we state the following Lemma and Corollary:
Lemma 1 ([31), Theorem 5.5). Consider a loss function C such that O S C(p.y) S L and Lipiscchitz with respect to the output distribution p and ground-truth label y. Suppose that the Adam optimizer with stabilization constant e E (0,1) 1) executed for T iterations with an initial random parameters R. training set S = { 1/s M batch data B [(3 y/ )=));=>))) and learning rate X obtain f R. The empirical risk Remp is defined as Remp (fBg,R) -i= (x)( yi) on a finite training set. The true risk Rove (JB5,R) ) is estimated with the empirical risk over the whole dataset that follows the distribution of the training set. The generalization error F[JBB,RR RR) Ravue (BBRRR) ) -Rmmp (J (JBR.R).
Then, we have the following generalization error bound with probability least 1 -:
2n (4
by 2
UTT² (1 (22 + L
(2g(2e)
(f S
(Tlog(2/()++
(7)
2N
The proof of Lemma I is [31. The following corollary compares the generalization error bounds for the learning algorithm which uses CE and MAF loss.
Corollary 1. Consider LLMs are trained using the CE loss and MAF loss separately over the same training set S, batch data Bs and other settings. Denote PCE MAF as the corresponding LLMs using CE loss and MAF loss. We huve the following inequalities:
R R
E (JB E (JBgRR)
(8)
The complete proof of Corollary is provided in Appx. C.I Corollary provides a tighter bound on the generalization error of Adam when we use MAF loss. Specifically, the generalization error of LLMs trained with MAF loss function is upper-bounde bounded by the generalization error of LLMs trained with CE loss. This result demonstrates that MAF loss can achieve better generalization performance compared to the CE loss. This indicates that our method, after editing for specific queries, adapt effectively to more generalized scenarios.
2.4 Memory Stability Preservation via Fisher Information Matrix
To address the LLMs parameter drift caused by inconsistencies among editing data, we design a method for preserving memory stability using weight matrix (i.e. Fisher information matrix). During the sequential editing process, the cumulative shifts in editing data from different domains also tend to lead parameter drifts in the L.Mss. Existing model editing methods primarily focus on the single-edit outcome, and ignore preserving parameter stability 40]. During the formation and stabilization of memorizing in the hippocampus, synaptic consolidation strengthens the connections between specific neurons to protect the stability of important memories [ Therefore, we employ the calculated
weighted Fisher information matrix (FIM) 229 23 to protect the LLMs. FIM constrains the update magnitude of parameters with significant influence on the model outputs, and permits more substantial updates for parameters with less impact on the model outputs.
Specifically, we update the parameter ò in Eq. to avoid affecting the existing knowledge, instead of directly constraining the updates of the parameter W. The motivation is we observed that, in sequential editing tasks, due to the domain gaps between the editing data, training ò tends to lead parameter drift and model instability. For the sequential editing data e = (31, T1, 01, of) and 62 (82, F2, O2 og), we need to satisfy both and 62 simultaneously. Thus, dey,eg eg is obtained by using the Maximum A Posteriori (MAP) estimate and Bayes' theorem:
& arg maxpma e2) arg max p(e2)))p((1e1) p(ez)
(9)
5

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Since we optimize ó, the probability p(ez) remains constant, indicating the distribution of the editing data 2g independent of 8. Therefore, the objective of 5% is
S = arg max p(ea))))p))) = arg max log + log p(gle))
(10)
The term logp(ez2) corresponds the objective of the negative loss function -Leg (0) in Eq. Thus, the optimization objective is further refined as follows:
Ser,02 arg max] - Leg (6) + log [6]e]]] = arg min Ce, (6) - log p(ole)))
(11)
However, the term p(ōle,) lacks straightforward interpretable form, which is intractable to compute its quantiles [29] Laplace approximation [41] approximates complex posterior probability distributions using Gaussian distribution. This approximation assumes that the posterior probability distribution can be approximated by a quadratic function around its maximum. Therefore, we utilize this approach to approximate the probability p(ole;) with a Gaussian distribution in general cases, leading to the following Theorem |
Theorem Assume that the ieh editing data e; (si, Tá, Os, 0,) and the posterior probability p(ò ea) smooth which reaches its maximum around 8,,- Then, it can be approximated by a Gaussian distribution N with mean 8%, and variance Fe. where Fe, is the Fisher information matrix of the L.M after editing es. Specifically, the approximation is given by:
p(ole)) ~ N (62,, F
(12)
O 1og p(olci) (
(
O log p(ole.) 08
Fe, F
(13)
05
The detail of proof is in Appx. C.2 According to work (1. we obtain F., by treating p(es) constant and ignoring the prior p(6), which makes derivative of posterior log p(ole;) and likelihood log p(e,()) equal. Based on the Gaussian distribution 12 and Eq. assuming that 5", for the (i + 1)th edit, we obtain log p()e())
A F..6.
((ge()))))
(14)
2
For scenarios involving more than two editing data, we obtain the posterior probability:
1 O Σ (A.F.)
log(őle1, 02. (..")l,,,..."".....)) =
(15)
Therefore, based on Eq. 6 11 and 15 for n times editing we formulate total training loss for d as:
Ls = LMAF + 1.5 5" (.FF.)))
(16)
2
where A are hyperparameters that control the magnitude of the Fisher information matrix loss, the number of sequential editing. We leverage Fe, to precisely control the magnitude of changes in each parameter of 6, which differs from conventional weight decay methods. Eq. 16 shows that parameters with higher Fisher information values exhibit lower update magnitudes when exposed to new editing data. This prevents interference between data from different domains and facilitates an effective separation mechanism. Note that Sec. 22.4furtter accounts for interference among different edits, which is not in conflict with the modeling approach presented in Sec. 2.3 but rather constitutes a complementary extension.
2.5 Progressive Memory Consolidation via Parameter Replay
Sequential editing of LLMs is prone to catastrophic forgetting of existing knowledge. Thus, we utilize long-term editing memory to progressively replay edited knowledge. When we edit LLMs learn new knowledge, the lack of consolidation of old knowledge disrupts previously learned representations. Memory consolidation in the hippocampus, which transforms short-term memories into long-term memories, relies on the reactivation and enhancement of neural activity patterns associated with previously experienced events through CA3-CAII I sharp wave ripples 226 [3] This process requires repeated reinforcement until the corresponding memory is stored in the cerebral cortex [28]. In

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typical model editing method, only short-term memory retains recently edited knowledge. Inspired by hippocampus mechanism, we derive a rigorous closed-fore solution to maintain "long-term editing memory" that replays all edited knowledge.
Specifically, according Eq. 4 note that the incremental matrix A considers only the knowledge pair (K, V) in single edit operation. In the sequential editing scenario, we extend this to n edits to obtain the following theorem:
Theorem 2. Assume that W after the in sequential edit denoted as W. The knowledge pairs associated with the inh edit is represented by keys K; and values V,, Let Co = Xo 6; = V- W, and Cov Cov_ = K2K5. de is hyperparameter. The convergence factor a; = i-1 (i 1) ensures the convergence of the sum of As and balances the degree of consolidation for different editing knowledge. Then it follows that:
W. W D.
(17)
A; 6:K, (Co +
+ KiK?)
(18)
Corollary 2 (Convergence). Given D as defined in Theorem 2 let a; = - (i 1) and the minimum eigenvalue of Co and the maximum eigenvalue of K,K; (Kę E RR)) are all at least I. Assume that for all indices S q K2 are mutually orthogonal (practical). Then the Frobenius norm lim ||alle converges.
The derivation of long-term editing memory in Theorem and the proof of the convergence factor os in Corollary 2 are in Appx. C.3 In this theorem, Cov_ sun represents the long-term editing memory, which is the sum of the covariances of all non-normalized keys K. Since the Cov __suummm_- = Σ KK" encapsulate all the input information from previous edits, we consider this is a form of parameter replay. Corollary 2 demonstrates that the introduced convergence factor effectively prevents the Frobenius norm of the edited parameters from surge. This approach does not require substantial storage or computational resources, which in contrast to traditional replay mechanisms [50]. By employing iterative method, we only need compute the Cov_sum by accumulating the K K from the previous editing step. The overall procedure of the HSE method detailed in the Appx.
B
Experiments
3.1 Performance Results on Sequential Editing
In this section, we compare our proposed method HSE, against other leading model editing baselines on two benchmark datasets. For detailed descriptions of the datasets, baselines, implementation and evaluation metrics, please see Appx. E.T E.2 and E.3
To validate the generalization of our editing approach, we conduct one-by-one sequential editing with 1,000 samples experiments on four open-source LLMs respectively: (lamma--Instrue (8B), Mistral- 7B-Instruct- V0.3, GPT-J (6B), and GPT2-XL (1.5B). As shown in Fig. 2 (a)-(d), the HSE method achieves superior performance across all LLMs and datasets. Compared to the best baseline, our approach demonstrates average improvements of 20.6% in generalization, 21.9% specificity and 17.3% in efficacy. For all models, most baselines suffer from catastrophic performance degradation due to model parameter collapse after multiple edits. Notably, the Llama3 and Mistral models exhibit particularly significant improvements across various metrics. This is attributed to their smaller F- norm of parameters, which makes other methods more susceptible to catastrophic forgetting (Fig. 9). Additionally, HSE exhibits the highest fluency and consistency, even surpassing the original model in multiple consistency evaluations. The enhanced specificity performance may be attributed to the fact that during the editing process, the LLMs revisit and refine their knowledge about the subject, leading to a deeper understanding. The excellent performance on both counterfactual and standard QA datasets demonstrates the effectiveness of our proposed approach.
We do not include a comparison with the AlphaEdit [Q and F-learning Instead, more detailed comparison and discussion are provided in Appx. and E.7 In addition, we provide case study in Appx. [F and time complexity analysis and performance comparison across various batch sizes for the full-batch editing scenario in the Appx. E.6
baselines in the figure.
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Figure 2: (a)-(d) present the performance comparison results of one-by-one sequential editing across four LLMs and two datasets Counterfact and ZsRE, using 1,000 samples. Eff., Gef,,ee,,,ppe. Flu. and Con. represent efficacy, generalization, specificity, fluency, and consistency, respectively.
3.2 Downstream Evaluation after Sequential Editing
To evaluate the general capabilities of edited LLMs, we tested Llama3 on six tasks from the GLUE benchmark [58] after sequential editing with CounterFact and ZsRE respectively. Details of the GLUE tasks can be found in Sec. 2
As shown in Fig. our proposed HSE method not only maintains but slightly surpasses the original performance of Llama3 across all six tasks even after 1,000 sequential editing steps. In contrast, other methods exhibit sharp decline in performance AlphaEdit effectively preserves most of the model's general capabilities, yet still demonstrates marked decrease performance. After editing with CounterFact data, the performance of MEND and FT-L methods across all tasks drops to zero within 100 steps. Moreover, MEMIT and PRUNE maintain performance until approximately 400~-500 steps before experiencing a sudden decline to zero. Following edits with ZsRE data as shown in Fig. 3 MEND and FT-L methods again exhibit rapid drop zero within 100 steps, while MEMIT and PRUNE show a delayed performance collapse, occurring around 600 steps. The reasons for the sudden performance drops of these methods are discussed in the ablation study part (Appx. E.5). Our proposed HSE method exhibits the following behavior. Given that the facts in CounterFact are counter to reality, the edited model may experience slight 1.42% decrease in general capability during testing. However, when the model is correctly edited with knowledge from the ZsRE QA dataset, its general capability shows average 1.67% improvement. These results indicate that our editing method HSE minimally impacts the model's general capability and can lead to slight enhancements when guiding the model with correctly edited knowledge.
zero. As special instance of HSE,
3.3 Sequential Editing Results on Practical Applications
To demonstrate the broad applicability of the proposed HSE method, we conduct experiments on three practical applications: hallucination mitigation (Fig. 4 healtheare knowledge injection (Fig. 5 and societal bias reduction (Fig. (). Experiments demonstrate that HSE achieves consistently superior performance across the practical applications compared to existing sequential editing methods, highlighting its strong potential for future development and real-world deployment (see Appx. E.4
3.4 Ablation Study to Validate the Hippocampalllik Design
To systematically evaluate the contribution of each component in our framework, we first conduct ablation experiments with intuitive performance comparisons (Tab. 2). Additionally, to justify the

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Figure 3: The general capabilities of the Llama3 model on the six tasks of the GLUE benchmark after editing with the CounterFact (a) and ZsRE (b) datasets respectively.
hippocampal-iike design of each module, we carry out further analyses, including visualizations of machine unlearning (Fig. 7 domain-speciffe knowledge update separation (Fig. 8) and the stability of long-term editing memory during replay (Fig. 2]. As shown in the experimental analysis presented in Appx. E.3] our proposed module not only significantly improves performance sequential editing, but also aligns well with the biological principles underlying memory consolidation in the hippocampus, thereby reinforcing the neuro- inspired foundation of our approach.
Notably, the Long-term Editing Memory (LEM) module plays a pivotal role mitigating model collapse and catastrophic forgetting. This is evidenced in Fig. [ where LEM effectively constrains the deviation of the parameter matrix's 's Frobenius norm, establishing it as a fundamental component compared to other modules. The Fisher Information Matrix (FIM) design serves to quantify the retention degree of prior knowledge. Fig. F demonsttaates that the FIM successfully safeguards critical parameter updates for edited knowledge while preventing interference across distinct knowledge updates. The Active Forgetting module dynamically regulates knowledge retention versus discarding. Ablation results confirm that AF contributes most significantly model generalization, finding that is consistent with the theoretical implications of Corollary[]
Related Works
Sequential Model Editing. Current approaches sequential model editing can be broadly cate- gorized into two paradigms: garameter-peserrving and parameter-modifyli methods. Parameter- preserving methods typically introduce auxiliary modules store edited knowledge [22 20 46 61] 68 (22. SERAC [46] maintains an explicit memory of edits and employs a classifier to determine whether to apply the stored knowledge during inference. T-patcher (22) proposes sequential model editing by continuously adding and fine-tume fine-tuning a small set of neurons in the final FFN layer to accommodate new edits. GRACE 20 records edit knowledge as key-value pairs within a learnable codebook, enabling persistent and scalable knowledge updates. WISE [61] introduces
memmmyy module that trained to retain previously edited knowledge. In contrast, mardifter-modiffing meth- ods follow a meta-learning [5 g or locate-then-edii paradigm [3
44
14,
10 40). for sequential
editing. MEMIT [44) enables efficient batch editing of factual lewl but tends to suffer from model collapse under sequential editing. Both RECT [14] and PRUNE (40] refine the update matrix through post-prec post-processing: RECT regularizes the update, while PRUNE controls its singular values to sequential editing. AlphaEdit [10] orthogonalizes newly injected knowledge with existing knowledge to mitigate catastrophic forgetting. This method is discussed in our work as a special case of our proposed replay mechanism. Most existing approaches to sequential model editing do not adequately mitigate parameter drift that arises when newly edited knowledge conflicts with existing knowledge, often leading degradation in general capabilities.
Bio-inspired Continual Learning. Recent advances in continual learning have increasingly drawn inspiration from biological systems to address catastrophic forgetting 11 39
24
56
27
16 59
71 70). Neuro-inspired adaptability models, fruit fly learning mechanisms employ selective memory
protection and forgetting balance stability and plasticity [59]. CLASSP 39] leverage synaptic

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plasticity principles by suppressing non-critical weight updates while promoting sparse learning. Replay-based approaches, BiRT [24]. Replay-through-Feeebeee [56] and Robust Rehearsal [27]. mimic memory reconsolidation in biological systems to refine past experiences. Additionally, GEM [77] and EWC incorporate episodic memory and synaptic consolidation respectively, to mitigate interference between tasks. These biologically inspired strategies highlight the potential of neuroscience principles in advancing continual learning paradigms.
Conclusion
In this paper, we propose a Hippocampal-like Sequential Editing (HSE) method, which designs the unlearning of obsolete knowledge, domain-specific knowledge update separation and replay for edited knowledge. Our experimental results show that HSE significantly outperforms existing model editing methods across multiple benchmarks. Under sequential editing conditions, the LLMs can almost entirely retain and even enhance its general capabilities. Theoretical analysis confirms that the HSE method exhibits tighter generalization error bounds, more stable convergence and higher computational efficiency. Furthermore, HSE facilitates significant practical advancements in mitigating multi domain hallucinations, updating healthcare knowledge and reducing societal biases for LLMs.
6 Acknowledgments
This work was financially supported by the National Natural Science Foundation of China (No. 62476283, No. 62376284).
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21

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Technical Appendices and Supplementary Material
Key Parameters and Descriptions
23
HSE Procedure
23
Proof of Main Results
23
Proof of Corollary
23
Derivation of Fisher Information Matrix for à
25
Derivation of Å and Long-term Editing Memory
26
Other Interpretation of the Update Matrix A
28
Experiments
29
Datasets and Evaluation Metrics
29
Implementation Details
30
Baselines
31
Sequential Editing Results on Practical Applications
31
Ablation Study to Validate the Hippocampal- like
.o..............................
32
E.6
Sequential Editing Compared to Full-batch Editing
................................
35
E.7
Comparative Analysis of Forgetting Forgetting- beigt
................................
36
Performance in Multi-hop Question Answering Scenarios
................................
37
Hyperparameter Sensitivity Analysis
37
Case Study
37
L Limitations and Future Work
39
H Potential Societal Impacts
40
22

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A Key Parameters and Descriptions
To facilitate the readability and comprehension of our work, we provide detailed deseription of the commonly used and key parameters. The specific descriptions are follows:
Nomenclature
Subject of the edited knowledge
Relation of the edited knowledge
Object of the original knowledge
0*
Target object of the edited knowledge
Random token prefixes
Input encoding of the knowledge before entering edited parameter
Output encoding of the knowledge after entering edited parameter
6
Incremental update parameter for v (Eq. 2
62,,0
Optimal ò when editing e2 while jointly considering both e and e2
A
Incremental update matrix for modifying the edited parameters (from Theorem 2
Fe
Fisher information matrix of the LLM after editing
Y
Lipschitz constant
Cov_sum Long-term editing memory
Balance the degree of memory and forgetting
X;
Control the magnitude of the Fisher loss
A Control the retention of original knowledge
a;
Convergence factor for preserving general capabilities
B HSE Procedure
To facilitate the practical application of HSE, we present a detailed exposition its specific algorithms and operational procedures. We illustrate the editing process using the most common scenario, one- by-one sequential editing. The process is illustrated in the algorithm] !
For the localization operation the algorithm, we employ established methods causal tracing for noise recovery and identify the edited parameters. These parameters typically located in the second linear layer of the FFN within the early transformer modules (57) of LLMs. In this procedure, we do not explicitly illustrate the editing process for different layers. The residual spread between layers is implemented using the formula --ー- where T. denotes the last edited layer and represents other edited layers. Detailed residual spread details can be found in the provided code and work [44
to perform
C Proof of Main Results
C.1 Proof of Corollary
Notation. Let E RIV be the output probability distribution of edited LLM. Let Ppos and Pneg be the positive and negative next-token probability (index ipos 'neg V) respectively. Let denote ground-truth label. || 1|2 is denoted as the fy-normalization. Considering an example (x, y) to be edited and the corresponding sample (x,, 3) to be forgotten, and for simplicity ignoring the
normalization factors [D] and [DI derive the gradients of LCE and Las AF with respect to p:
JLCE
[VLce(p,
(19)
0p
23

---

Algorithm HSE algorithm
Require: Initial LLM fo. Editing dataset D {e; = (8i, Ti 01 o2))= Templated prompt p(-).
Random token prefixes ditribution Mpretix Wikitext T, Hyperparameters λc, (as)
Ensure: fn
1: Locate edited parameters in LLM:
2: Wo t Causal trace(fo)
3: Get general knowledge in original LLM:
4: Ko < SampleWiki(T,10 10%)
5: Co Xg Kg
6: Initialize long-term editing memory:
7: Cov_5wnn f-
8:
Fe, +-
9: for = to n do
10:
{ sid
Upeefis
11:
Compute key vector: k; - - j= Enow; (x, 2 8:)
12:
13:
Compute value vector:
2 + Wi-ik; ò,
15:
& arg min log pa(ot r(),,r:)) (1 aa) po(ālp(s;,r.)) + 10-00 ΣE (N)Fe))
16:
Fe, +
Olog 06
a @llg
T
64
17:
Compute update:
18:
A; + ō,k, (Co + a,Cov_sumi s + kick )-1
19:
Long-term editing memory update:
20:
Cov sum; + _oo_summ-1- k,k? Apply edit:
21:
22:
W; W + A,
23:
end for
24:
Replace fo)
MLMAF
a² Pioc
(1 ax)
(V
+
(20)
0p
(1 Pricy
Proof Overview. According to Definition [ we establish that both LCE and LMAF satisfy the Lipschitz property during training. By Corollary 2 we find that the Lipschitz constant MMAF for AMAF is smaller than the Lipschitz constant YCE for Lcg. According to Lemma D we know that the generalization error bound influenced by y and the maximum value of the loss In the subsequent Corollary we demonstrate that the proposed loss LMAP has tighter generalization error bound compared to LoE-
Definition (Lipschitz Property). A loss function C(p,y) is y-Liscchitz (admissible) with respect to the ffr y 2 and VpI, P2 E IR Vl we have
(C(p..y) L(p2.y)) 7llp P2ll2-
(21)
From Eq. 19 20 and a practical point of view, when training such that Ppos 0.5 and Pueg 0.5, observe that both VV 1) and [ (.CCFpp.y))) y) are less than 4. This implies that under this condition, both Loe and CMAP satisfy Lipschitz Property. Therefore, all subsequent discussions are based on this condition.
Lemma Given that the CE and MAF loss functions are -ips-his,, "MAP-Lipschitz with respect to the output distribution p. with respective upper bounds LCE and LM AF, the following inequality holds:
& TCE.
(22)
LMAF S LCE.
(23)
24

---

Proof. According to the Eq. 19 and 20
we subtract the two equations to get
(a² -1 (1-a)²
VLMAF (p. 1)
(CCc(p.y))1
(24)
Pros
(1 - Pneg)
From the properties of the probability distribution, we have Ppos + Pneg I 1. Therefore, it follows
that 1-paeg
Consequently, we can derive:
Ppne
(a² - 1) (1- [1= a)²
A
(25)
Pios
(1 preg) )2
(a² - 1)
(1 a)
Pips
Pros
a 1 + (1 - x]
Pion
20(a - 1) 5.
(26)
From the sign of A we can conclude that I|VLM yp. is less than [V y) which
means "MAF S YCE. Because Ppos + Pneg < 1, follows that - log(1 Pneg) < - log Pposs we derive that
fMAP = -a log Ppos (1 ax) log (1 - PPeeg)
I a log Ppos (1 a) log Ppos
log Ppos LCE.
(27)
Therefore, from relation 26 and 2
we prove that ?MAP S ICE and AA S. CCE.
n
Lemma (3II, Theorem 5.5). Consider a loss function such that 0 S. C(p. y) S L and --iischiitz with respect to the output distribution p and ground-truth label y. Suppose that the Adam optimizer with stabilization constant e. E (0, 1) is executed for T iterations with an initial random parameters R. training set S = {<x; yo ya) }. batch data B = ((1,,((,), y:) )m and learning rate X to obtain f
The empirical risk Remp is defined as Remp (fBs.))= Σ (xx)) y1) a finite training set. The true risk Rome (fBB.R) is estimated with the empirical risk over the whole dataset that follows the distribution of the training set. The generalization error E(S (()BBRR) = Rune (fBs - -Remp (g.g.)).
Then, we have the following generalization error bound with probability at least 1 -e
E (fDg. R) S 2n
(4
(4))
by
+ 8T (1 /2N log(2/c)) + L
l2g(2e)
4
VTllg
(28)
2N
Corollary 1. Consider LLMs are trained using the CE loss and MAF loss separately over the same training set S, batch data Bs and other settings. Denote rCE BR J as the corresponding LLMs using CE loss and MAF loss. We huve the following inequalities:
MAF
(MAA (F
S E (FEE.)
(29)
Proof. As shown in Lemma [ the generalization error bounds for different loss functions under the same training process are determined by y and L. Given that TMAF S IC in inequality 22] we know that the first term in the generalization error bound for MAF (as given in inequality / is smaller. Additionally, inequality 23 indicates that the second term in inequality of MAF loss is smaller than the corresponding term of CE loss. Hence, we can conclude that E
MAP JB.R
E (f...R).
C.2 Derivation of Fisher Information Matrix for
Theorem Assume that the in editing data e; (s;, Fi, o, of) and the posterior probability p(5) e,) is smooth which reaches its maximum around 8",-- Then, it can be approximated by a Gaussian distribution N with mean & and variance F-11, where Fe; is the Fisher information matrix of the LLM after editing e, Specifically, the approximation is given by:
p(ōlez) ~ (62, Fe.1)
(30)
O (logp(0ee))
i log p(8) (i)) 05
Fe, =
(31)
86
25

---

Proof.
Given the editing data and 6++1, according to Eq. 11 we have
Sene = arg min (6) log (())))))]
(32)
We define 6% = arg max p( peaked around its point maxima. Let f(ā) = log p()e() and the
first derivative satisfies oc, is given by
2fíó] a6 b., = Therefore, the recond-order Taylor expansion of f()) around
8 f(6)
f(6) ~ f(0,)) + 82,)
(5 5%,)
(33)
026
Hessian
Next, since f(ō2.)
can be treated as a constant, we express the Hessian matrix as
o f(5) g
Based on the properties of the Fisher information matrix 48 we
82
know that
8²
[())
((250)
(
(.1(6)
E
8
E
05
08
=
Fes-
(34)
62
Therefore, from Eq.
33 and
34
we obtain
(( exp f(8..) exp
52,)² Fe, (6- -6.))
(35)
Since Eq. 35 conforms to the form of Gaussian distribution, we derive the Laplace approximation
of the posterior probability
p(ōle.) ~ N (62,, Fe!t)
(36)
From results
34 and 36 we can conclude that Theorem holds.
C.3 Derivation of and Long-term Editing Memory
The derivation and proof of and long-term editing memory, presented in Theorem
follows.
Theorem 2. Assume that W after the ith sequential edit denoted as W., The knowledge pairs associated with the iz edit is represented by keys K₃ and values V,, Let Co = X 6, K2Kţ. de is hyperparameter. The convergence factor a; = - (i > 1) ensures the convergence of the sum of A and balances the degree of consolidation for different editing knowledge. Then it follows that:
V. - W,, and Cov --smm-==
W: W + A;
(37)
8, K. (Co +
+ K
(38)
Proof. Step the Inductive Step.
Base Case. When = 1, base case in Eq. has been proved, so we only need to prove
Step 2 Induction Hypothesis. Assume that times sequential editing satisfies
the optimal parameter Wn-1 through n 1
W Ko Vo
W. Ki Vi
Wn-1Ka-1- = =V-1 Step 3 Inductive Step. When i = the ideal optimal parameter W,, is supposed to satisfy one additional condition W,K,, Kn V,, Then
(39)
W. Ko... ..o..... Kp] = VVo... Vnl
(40)
normal equation: W. Ko ... Kn] Koo.. Kn] [1o Val Ko
Kr]
(41)
which expands (W,-1 (Ko
+... K, kT² VoK
KT
(42)
W Wn-1(KoK +... K. K.
(KoK")
+... K k?) )- VoKg +... V.K
(43)
26

---

from induction hypothesis 39 simplify the Equation 43
we know that Wn-1K,K? = VK? sisn n- 1), so we can
W K
(KoKo +... + K, VnKa
(44)
(KoKo +... + K, VnKK -n
(45)
6, (KoKö + +... K.K? +... KnKK)
(46)
Given that each equation in inductive hypothesis 39 can have distinct factor, the term K, K in Eq. [66] share the same factor. For briefness, we set the convergence factor a; and variable
Cov Cov_sum;
Ej=1 K,Ke" and Co - AcKoKY hence
An 8n K (Co + CypCoosuunn- + K KTT""
(47)
It suggests that when the Eq. 38 still holds true.
Based on Stepl and Step2, we can conclude that for all E Z+
A K (Co + a;Cov_ssmii- + K W. Wi-1 + A.,
(48)
where Cov_summ E (i 1), Cov_sumg = 0 and 6; V; W
Given the continuous updates to edited parameters by the A, it is essential to set a; to an appropriate value ensure the convergence of the of A; and prevent the edited parameter surge. To simultaneously ensure the convergence and maintain editing efficacy, we set a; as decremental factor for 1. The specific form of a; is given as follows:
0+ 0== i-1'
Corollary 2 (Convergence). Given A; as defined in Theorem let a; = 1-1 (i 1) and the minimum eigenvalue of Co and the maximum eigenvalue of K K, (K₃ TR) are all at least I. Assume that for all indices i S K2 are mutually orthogonal (practical). Then the Frobenius norm lim ||aillF converges.
n-+00
Proof. Based on the results presented in Theorem 2 we derive the following recurrence relation:
Wi- + W,-1 + S.K. (Co aaaaaoossuuu-- sum + K.K
(49)
During the training process, we impose the constraint ||& = 0.75 WillK,llF. Based on the properties of the Frobenius norm, when i > we know that:
||W:1 + Adlp S ||W | (Co + a;Cov_sumi
(50)
< ||W- 0.75 F. |K. [|e
(Co + a:Cov CacCooosuuu-i-e K.
)
P
(51)
[|WW= (1 (1+0.75 || || (Co a,Cooe_summaa + K.K,) |11))
(52)
Given that K; are mutually orthogonal when S it follows from Weyl's inequality that for i >> q. the minimum eigenvalue of (Co + a;Cov_sum;-1 + K2K?) is least " Consequently, the maximum eigenvalue of its inverse matrix at most g. Therefore, the Frobenius norm of the inverse matrix bounded as follows:
q.
3/22
[C. + a,Cov_summ_ K.K?)
A3
(53)
Since K; represents the input encoding before entering the editing layer and varies little with i, it we derive that for i > q:
can be treated constant Pk. Consequently, using Eq. [2 and
53
3/2
W Aill S <|[W-il
0. 0.75px
(54)
3/2
Walle S ||Wale
1
0.75px
(55)
n
27

---

A + oc, it is evident that right part of Eq. 55 converges to | p exx (0.75pk Conse- quently, the Frobenius norm || |[W, l| p is monotomically increasing and bounded above, which ensures its convergence. However, as - oo although the model retains its general capabilities, it becomes increasingly challenging to integrate new knowledge. This indicates that larger q. which corresponds to greater scale of model parameters, significantly enhances the model's 's ability to incorporate new editing knowledge.
D
Other Interpretation of the Update Matrix An
According to the close form of the A., we also observe that A, adheres to the following relationships in AlphaEdit [10]:
Kn =0, K prc =0,
(W + K, =Va,
(56)
where Kyre, [ki]k2l-. [kn-1] represents the concatenation of all keys from previous editing steps. These equations indicate that not only A, satisfies W,, K₁ = Vn, but also orthogonal to all previous keys inputs. Therefore, the incremental closed-form A; obtained at each editing step has no impact on the previous editing inputs and the model's internal preserved knowledge. Our approach achieves long-term memory from the perspective of memory consolidation but introduces broader and more general convergence result. When the inductive hypothesis 39 is not applied, Eq. |3 in Appx. be simplified as follows:
can
AnoKoo + +... (...+KKK,)) KnK,)) Σ (WK - Wn-1Kik(),
(57)
An
8/ K. (Cu + CCc_oooummma + KnK,) k
(58)
= An +
8 K. (Co + Can_soonum_______ + K Kn
(59)
where _ - V; W,, Our convergence factor a; not only facilitate the convergence of the norm of A, but also through a larger initial value ensure that for earlier edits where i < n, the relation a; W,W,1KK effectively minimizes interference with prior knowledge and enhances its retention. Nevertheless, to maintain the model's capacity to edit new knowledge, we adopt a decaying schedule for oş allowing sufficient flexibility to accommodate newly edited knowledge. Therefore, we present the experiment performance under various convergence factor a settings Tab. [
V, approximately holds, resulting in 6;-1 approaches 0 more closely. This
Table 1: Impact of Different range of o4 (i 1) sequential editing performance across 1000 samples using the HSE Method. The best performance highlighted in bold.
Counterfact
ZsRE
Range of
Efficacy
Generalization
Specificity?
Flueney?
Consistency
Efficncy!
Generalizution
Specificity
AlphaEdit
98.20.ar
91.17.0o
62.15
622.
32.40.12
95.60
93.14
40.05
wro
98.62
92.73
76.
624.49
32.35
97.60
93.13
39.12
n/(i- 1) (Ouns)
92.
87.50
632,76.am
32.89
99.28
96.78
41.90...
9/2(
99.20 29x2
92.82
81.75
32.05
98.80.a.
96.01
38. 48.42
2n/3
95. 20.40
88. 88.72
630.76a
31.40laa
96. 06.99
95. 39.39
41.05
96 24.
87.90
629.49e:
32.12as1s
95.24
94. 68
41.42aam
In addition to set a; = n/(ii 1), we introduce o; a linear form = linear form satisfies n - i n/(i - -)),, thereby also ensuring the convergence property. As shown in Tab. [ the performance of the model with the linear form of a; exhibits certain degree of degradation. This degradation may be attributed to the excessively high values of as which can impair the model's is ability to effectively edit new knowledge.
- =
For E [2, the
28

---

E Experiments
E.1 Datasets and Evaluation Metrics
As shown in the task formulation 2.1 for the ith editing operation, the editing data e; (5,, os, of). The it, editing operation on the LLM results fi. The data requiring forgetting, characterized by the model's originally high-confidence predictions, is present across all of the following datasets.
Counterfact dataset [43] presents challenging cloze task for model editing. Due to its counterfactual property, where the data content contradicts established facts, LLMs typically exhibit lower initial performance this dataset. Unrelated editing data instances are constructed by replacing the subject terms with others that share the same predicate.
ZsRE dataset [55] is question-answering answering (QA) dataset designed to evaluate the performance of model editing. Each editing sample includes a subject term and an answer as the target for editing, alongside rewritten questions for assessing generalization and specific questions for evaluating specificity. The dataset employs back-translated questions as rewritten questions and includes natural questions unrelated to the edited data assess specificity.
HalluEdit dataset [21] is a meticulously constructed benchmark specifically designed to assess the effectiveness of model editing in rectifying nonfactual information generated by LLMs. The dataset comprises a comprehensive collection of over 6,000 hallucinated answers across domains and 26 topics, ensuring diverse and robust evaluation framework. Unlike previous datasets, HalluEdit ensures that LLMs generate hallucinated answers to the evaluation questions before any editing interventions, thereby providing more accurate assessment of the editing methods' efficacy.
SafeEdit dataset 600 novel benchmark designed to investigate the detoxification of LLMs through model editing. It encompasses nine distinct attack domains. Given that the output responses are often lengthy, the success of detoxification can typically be determined from the beginning of the response. Therefore, we simplified the original dataset by retaining only the first sentence of each response and evaluated the efficacy of model editing using standard metrics.
GLUE benchmark [58] comprises six tasks designed to evaluate the general capabilities of natural language models:
SST (Stanford Sentiment Treebank): This task involves sentiment classification of movie reviews, determining whether the overall sentiment is positive or negative.
MRPC (Microsoft Research Paraphrase Corpus): This task evaluates whether a given pair of sentences semantically equivalent, focusing on paraphrase detection.
MMLU (Massive Multitask Language Understanding): This task measures the multitask accuracy of text models across a wide range of linguistic tasks, assessing their versatility and robustness.
RTE (Recognizing Textual Entailment): This task determines whether a premise sentence logically entails a hypothesis sentence, evaluating the model's 's ability to understand logical relationships.
COLA (Corpus of Linguistic Acceptability): This is single-sentence classification task where sentences from linguistic theory books and journals are classified as either linguisti- cally acceptable or unacceptable.
NLI (Natural Language Inference): This task requires the model to infer the logical re- lationship between pairs of sentences, classifying them as entailment, contradiction, or neutral.
Counterfact Metrics. Given that the Counterfact dataset contains responses that are counterfactual to the factual answers, Counterfact metrics focus comparing the probabilities of counterfactual versus original answers. The evaluation metrics for the Counterfact dataset are defined as follows:
Efficacy is defined as the proportion of requests where the probability of the edited model f. output of (target_new) exceeds that of the o; (target_truue, when making predictions based on (s;,T)). Tz).
E, [o] (si,rr) Pr. (ss,
(60)
29

---

Genralization is defined as the proportion of paraphrased prompts E paraphrases($; r;) where the probability of the edited model output of (target_ner new) exceeds that of the oN (tar- get_true).
E, (epeparepllooeeeee..))
1 1p] > 15, rr]]
(61)
Specificity is the proportion of neighborhood prompts E neighborpromptslle r;) where the model assigns higher probability to the correct fact O,. [ y] P, pl]]
E, Sppeeeighorrrompphlcc
(62)
Fluency measures the extent of excessive repetition, common failure mode in model editing, by analyzing the entropy of n-gram distributions.
Σ (2(k))ogg (a(k) +
4
Σ (a(k) log, log, (a()..
633
3
Here, g.(-) is the n-gram frequency distribution.
Consistency measures the coherence of f free-form generations. To compute it, we first prompt f; with a subject s; then calculate TF-IDF vectors for both fi (s,) and a reference Wikipedia text about of The resemblance score (RS) is defined as the cosine similarity between these two vectors.
co8 < (F-)DF(f():)),, (i-iex((i))iex))))))
(64)
ZsRE Metrics. The evaluation metrics for the ZsRE dataset are defined as follows:
Efficacy is the proportion of edits for which the model achieves top-1 accuracy.
E2 o = arg max maxP,,le [o (ss,
(65)
Genralization is defined as the top-1 accuracy of the model on rephrasings of the (8t,
E, [peperaphrreetteurr
oo mag 5 pl]
(66)
Specificity defined as the top-1 accuracy of the model on neighbor prompts of the (s,, rz).
E, B
Appeeiighborpropptllsvvr [o arg max 1o rl]]
(67)
HalluEdit and SafeEdit Metrics. We employ evaluation metrics that consistent with ZsRE for both Efficacy and Generalization, given that both are question-answere datasets. However, since questions unrelated to the editing data in HalluEdit and SafeEdit do not have corresponding answers, we substitute the Locality metric for the specificity metric used in ZsRE
Locality is evaluated by the ratio predictions made by the edited model fi on neighbor prompts that remain unchanged compared to the predictions made by the original model f.
E, [Eppeneighborrromppsssrrr [ (o p] = Fo pl]]
(68)
GLUE Metrics. GLUE employs the Fl score unified evaluation metric. For more detailed information, please refer to (58).
E.2
Implementation Details
We conduct experiments on an A100 80GB GPU. Each model sequentially edits 1,000 data instances in approximately 5 hours. For the sampling of (Ko, Vo) in Sec. 2.1 computed using dataset of 100,000 samples extracted from Wikipedia. For the hyperparameter settings and implenmentation de- tails of the baseline methods, refer to the configurations used in MEMIT 44 and EasyEditor [62] The implementation details for our proposed method are as follows:
and OpenBioLLM-99 apply editing to layers [4.5,6,7.8]. Specifically, the update norm of i constrained to 0.75 times the norm of the original output representation to ensure controlled modifications. The iterative process for updating 5 capped at maximum of 25 steps, with learning rate le-1. To manage the trade-off between retaining previous knowledge and incorporating new information, we set the memory factor in Eq. a to 0.8. Additionally, the Fisher information matrix coefficient hyperparameter in Eq. 16 X; is configured to le-1, while the hyperparameter de of Co is set 15,000. These settings collectively ensure a balanced approach to model editing, maintaining stability and accuracy throughout the process.
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Mistral-7B-Instrutt-ddd The primary different configurations lies in the Fisher information matrix coefficient hyperparameter in Eq. [ X is configured Se-1.
GPTJ-6B The primary different configurations lies in the editing layers [3.4.5,6.7,8] learning rate Se-1 and Fisher information matrix coefficient hyperparameter in Eq. [6 X; is configured to 5e-1.
GPT2-XL applies editing to layers [13, 14, 15, 16, 17]. Specifically, the update norm of ó is constrained to 0.75 times the norm of the original output representation ensure controlled modifications. The iterative process for updating á is capped at a maximum of 20 steps, with learning rate 5e-1. To manage the trade-off between retaining previous knowledge and incorporating new information, we set the memory factor in Eq. 6 a to
0.8. Additionally, the Fisher information matrix coefficient hyperparameter in Eq. 16 Ag is configured to le-4,
while the hyperparameter Ac of Co is set to 20,000.
E.3 Baselines
For the baseline settings, we considered representative works in model editing, including parameter preserving methods GRACE [20] and parameter-modifying methods FT-L [44], MEND [45),
ROME [43, MEMIT [44), PRUNE [40], RECT [14) and AlphaEdit [IO]. Since our proposed
method also falls under the category of parameter-modiffing model editing, we primarily compare
with other methods within this category. For detailed implementation specifics of the baseline, please refer to [62].
FT-L applies fine-tuning to the identified editing layers using weight decay.
MEND performs concurrent edits by accumulating gradients from all edit examples and then passing them together through the hypernetwork.
ROME employs a "locate-then-edd" then-edit" approach, first identifying the layers that influence factual predictions and then performing rank-one one edits on the edited parameters.
MEMIT as an extension of the ROME method, enables batch data editing across multiple layers. simultaneously.
PRUNE control the changes in the condition number the editing parameters by restraining the condition number of the update matrix A, thereby protecting the model parameters.
RECT prevents overfitting by regularizing the update matrix A during the editing process, thereby avoiding excessive changes to the original model parameters.
GRACE maintains a discrete codebook to record key-value pairs during editing process, updating and adding elements over time refine the model's predictions.
AlphaEdit projects the incremental matrix onto the null space that preserves knowledge before applying it to the parameters.
E.4 Sequential Editing Results on Practical Applications
Hallucination mitigation. To validate the practical capabilities of our proposed model editing algorithm, conducted experiments across nine domains using the Llama3 hallucination dataset HalluEdit (21] and compared our results with competitive baselines. The evaluation metrics used include Generalization, Locality and Efficacy. Notably, Locality measures the output consistency between the post-edit and pre-edit models on questions unrelated to the edited knowledge. For detailed information on the datasets and evaluation metrics, see Appx. E.
As shown in Fig. 4 our proposed method effectively mitigates hallucinations across various domains. Compared to the best baseline, approach demonstrates substantial overall improvements, with specific gains of 28.5% in Generalization, 35.7% in Locality and 2.8% Efficacy. This underscores the practical significance of our method in two key aspects: (1) Robust performance across multiple domains. The ability to simultaneously edit data across diverse domains with high performance indicates that our method possesses strong generalization capabilities. (2) Effective mitigation of hallucinations in LLMs. Our approach effectively addresses hallucinations LLMs while preserving up to the maximum extent the original performance characteristics of these models.
Healthcare knowledge injecting. addition to experiments on the general Llama3 model, we perform tests the specialized healthcare models Aattaa--loo----AAAA and OpenBioLLM-8B to
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Generalization
Locality
Efficacy
94
50
70
Score (%)
Gengr
Performance comparison results our proposed HSE method on the Llama3 across domains of the HalluEdit dataset.
Figure 4:
demonstrate the applicability of our method in professional domains. Using the health domain of the Halluedit dataset for editing, as shown in Fig. (a) and (b), we observed average improvements of 61.89% in efficacy and 49.88% in generalization compared the original model. Since the Locality of the original model remains unchanged, it is represented as 100 and not displayed in the figures. Additionally, Locality was largely preserved. These results indicate that HSE holds significant promise for correcting biases in domain-specific knowledge, highlighting its potential applications in specialized fields.
Origina
Dr Original
IH4
100
0116
100
$3,45
66,E7
HSE (Quis)
HSE
80
80
Performance (%)
Perforaance (%)
60
50
3729
15,01
30
30
23.05
10
10
1.0
Efficacy
Generalization
Locality
Figure 5: Comparison of Editing Performance for the healthcare LLMs Llama3 Aloe- Moc-8B-Alpha (a) and OpenBioLLM-SB (b) on the Health Domain of the HalluEdit Dataset.
Societal bias reduction. As shown in Fig, we evaluated our approach the SafeEdit 660 dataset, which encompasses a variety of attack prompts and responses related to ethical violations, illegal activities, and social discrimination. The HSE method not only outperforms existing approaches but also shows significant improvements over models without forgetting mechanism. By enabling the model to proactively forget harmful responses, thereby further validating the superiority of our proactive forgetting strategy. This indicates that the HSE method is effective in mitigating harmful content, thereby enhancing the societal safety of LLMs. However, exhibits a slight decline Locality compared to the original model, which may be attributed an increased number of rejection responses during the editing process.
observed more pronounced performance gains,
E.5 Ablation Study Validate the Hippocampal-like Design
To demonstrate the effectiveness and rationality of our method, which is inspired by the hippocampus, we conducted ablation studies on both performance comparisons and visualizations. As shown in Tab. 2 our proposed method achieves optimal performance when all modules are included. Specifically, for editing CounterFact data, the active forgetting of machine unlearning component primarily affects efficacy and generalization. Removing this component leads to a 3.4% decrease in
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100
Original
52.86
41.95
100.00
MEMIT
82.05
73.26
5,24
Performance (%)
60
GRACE
90.71
41.68
58.12
40
W/o
92.58
80.56
70.06
20
HSE
97.54
92.83
73.70
Efficacy
Generalization
Locality
Figure 6: Heatmap illustrating the performance comparison of various methods on the SafeEdit dataset. The notation "w/o F" indicates that no forgetting mechanism was applied the harmful data instances.
efficacy and 3.65% decrease generalization. The Fisher information matrix module significantly influences specificity, removing it leads to respective decreases of 5.4% in specificity. Most critically, the long-term editing memory module plays indispensable role, as its removal causes substantial drops across all performance metrics. When editing ZsRE data, removing the active forgetting module results in 3.03% drop in efficacy and 2.55% drop in generalization, while removing the Fisher information matrix leads to 1.8% decline in specificity. Removing the long-term editing memory module again causes significant performance degradation across all metrics. Additionally, designed supplementary experiment where we replaced the long-term editing memory with an experience replay module from continual learning [50], aiming to mitigate catastrophic forgetting by replaying only subset of long-ferm editing memory. Results indicate that experience replay offers some improvement in sequential editing performance but remains significantly inferior to incorporating the long-term editing memory. Our proposed HSE method achieves efficient and precise editing through global lightweight replay.
Table Ablation study results of the HSE method. This table presents ablation study results HSE method, detailing the contributions of individual components. AF: Active Forgetting module, responsible for actively forgetting specific information. FIM: Fisher information matrix module, controlling parameter updates preserve important knowledge. LEM: Long-term Editing Memory module, reinforcing edited knowledge and preventing parameter prolifcration. ER: Experience Replay module, used continual learning to mitigate catastrophic forgetting by replaying a subset of data.
Counterfact
7sRE
Edit Mode
Efficuyy
Generalization"
Specificity!
Fluency!
Comsistency
Efficucy!
Generalization
Specificity
HSE (Ours)
99.60.mm
93,80
87.50
632.762041
32.89 .oz:
99.28 ous
96.78..u4
41.90
w/o AF
96.20
90. 15
86.40
628.19ar 19
30.85
96.25
41 41.06ea
w/o FIM
98. 10
92.04
82. 10sess
a2ss
31.06200
99.02.xxx
95. 14
40. 10
w/o LEM
60.85
55.62
53. 18
362 a4124
4.53
10.05
6.21 aoa
9.20
w/o LEM, w ER
81. 16
73.50
76. 10
518.62aum
14.29
42.50amas
38. 72
26. 14
To visly validate the rationale behind our method, which is designed based on the hippocampus' s trisynaptic circuit, analyzed the visualization results on CounterFact data after removing each of the three modules. Specifically, for the active forgetting module of machine unlearning, shown in Fig. 7 we analyze the model's 's performance on generalization questions. The average probability of tokens across three types of token probability (editing, forgetting, and others) is examined. "editing" refers to the editing target tokens that need to memorized, "forgetting" denotes the tokens that need to be forgotten, and "others" indicates tokens that are unrelated to the editing process. This analysis helps assess the model's 's existing knowledge level regarding generalization. The results indicate that, given the counterfactual nature of the data, the model initially exhibits relatively high average
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probability for forgetting knowledge. After editing without the active forgetting, while the probability for editing tokens increases significantly, there remains considerable likelihood of encountering forgetring tokens. However, using our proposed HSE method, the average probability of forgetting tokens drops substantially. These findings support the rationale our design, inspired by LTP for memory and LTD for forgetting within the hippocampus. The mechanism of active forgetting, akin to LTD forgetting suppression, effectively removes inconsistent knowledge from the model.
pre-edit
w/o forgetting
F-leaming
HSE
Average Probability
onthers forcetting edtting
foreetine
others forpettng
forgetting
Rangel to
0.4
Counterfact Z5RE
Counterfact ZsRE
Counterfact ZsRE
Counterfact ZsRE
Figure 7: Visualization of the average probability of generated tokens in pre-edit, w/o forgetting. F-learning 47 and HSE conditions.
To demonstrate the ability of the Fisher information matrix effectively distinguish data from different domains, we conducted visualization experiment using art and health domain data from HalluEdit. Specifically, the diagonal values of the Fisher information matrix indicate the influence of each parameter on model outputs. Therefore, for incremental update parameter in Eq. 2 we identified and highlighted the top 100 values from its fisher information matrix. As shown Fig. 8 (a). after editing with our proposed method HSE, the Fisher values for 5 in the two domains exhibit distinct differences. contrast, Fig. (b) without using the fisher information matrix as a constraint, the distinctions between domains become less apparent, potentially leading to confusion. These visualization results support the effectiveness using the Fisher matrix, akin to the pattern separation mechanism in DG, in distinguishing data from different domains.
Art
Health
Diagonal Element
Range: to
453
810
1820
453
910
2219
2730
3205
4003
Parameter Index
Parameter Index
(b)
Art
Health
Diagonal Element
Range:
910
Lbor
1020
2730
2275
stes
2010
Parameeer Index
Parameter Index
Figure Visualization of the top 100 values
Fisher information matrix diagonal elements for
the N parameters under the (a) HSE method and (b) without Fisher information matrix constraints, respectively.
Finally, we analyze the F-norm of edited parameters to elucidate why long-term editing memory effectively maintain model performance. shown in Fig. 9 for methods without long-term editing memory, the F-norm of parameters exhibits sudden surge after several hundreds of editing steps.
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In terms of time complexity, we compare the scenarios of a single edit (one-edit) and times edits (n-edit), as shown in Tab. 4 Let b denote the batch size for each edit, and q be the dimension of input embedding k. Typically, we have 6 < < x b. Building on our previous analysis and proof regarding the computation of A,, matrix multiplication has a complexity of (g²6),, and matrix inversion has a complexity of Oga²). Therefore, for one-edit operation, the time complexity of both HSE and MEMIT Iall are Ogq2b + )²) Given that real-world edited data is not generated all at once, we further compare the time complexity for n times edit operations. Since our proposed sequential editing method only requires incremental updates to the long-term memory parameters and new edit data, the time complexity after edits is O( bgbb + n q²), which simplifies to O(n). Furthermore, the calculation of the Fisher information matrix mentioned remains at the level of complexity O(n). Consequently, the overall time complexity of the HSE method is also of the order (n)). In contrast, MEMIT foll necessitates recalculating the representations for all edited data at each step, leading to a In contrast, for our HSE method, the F-norm of edited parameters increases much more gradually, Additionally, we provide a theoretical proof in Corollary 2 that establishes an upper bound on the F- norm after multiple editing steps. These findings indicate that long-term editing memory consolidates the model's 's existing knowledge and prevents edited parameter surge, thereby maintaining stable performance. Moreover, we observed that the larger the F-norm of the original LLMs, the more "resistant to editing" they become, allowing them to maintain their general capabilities even more editing iterations.
Ulama3
GPT.
Hisrol
120
120
HEE long torn
110
200
100
Firom surge
F-form
800
100
180
00
40-
160
500
750
1000
500 Stop
750
250
500
1000
Step
Stop
Figure 9: Line chart showing the changes in F-Norm values for the HSE method and without Long-term editing memory.
E.6 Sequential Editing Compared to Full-batch Editing
Given that MEMIT allows for batch-editing of all data at once 44 and serves as the foundational method upon which other parameter- parameter-modifying approaches are based, we compare our proposed sequential editing method HSE with the full-batch editing approach MEMIT full in terms of time complexity and performance to highlight the advantages of our method.
time complexity of On g²b + n q²²) after n edits, which simplifies to O(n)). Therefore, the number of edits increases, the time complexity of MEMIT fall approaches n times that of sequential editing.
In addition the qualitative analysis of time complexity, we provide quantitative measurements of the actual computational time consumption, as detailed in Table Our additional experiments report running time with increasing edit counts, demonstrating that the time requirements grow linearly with the number of samples and that our model's computational time remains lower than the baseline MEMIT Furthermore, the time consumption of the FIM module scales slowly with increasing edit counts, following ((n) complexity.
In terms of performance comparison, we conduct experiments on counterfactual data using both HSE and MEMIT with 1,000 and 10,000 samples. As shown in Tab. 4 for the 1,000 samples, our method HSE outperforms the MEMIT full approach across different batch sizes. Specifically, with I batch size, our method demonstrates superior efficacy and generalization, while 1,000 batch size leads to better specificity. To validate the scalability of our method to larger datasets, we also test it on the 10,000 samples. Our method maintains its superiority over MEMIT at both batch sizes of 10 to 10,000. At 10 batch size, the performance is particularly strong in terms of efficacy and generalization, whereas a batch size of 10,000 shows enhanced specificity. Although overall
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Table 3: Comparison of training time across different methods and sample sizes.
samples
HSE(Ours)
MEMIT full
w/o FIM
w/o Active Forget
10
Imin
3min
Imin
Imin
100
10.5min
65min
10min
11min
1000
101min
850min
97min
100min
3000
251min
242min
252min
performance slightly decreas with increasing data volume, our method still maximally preserves the model's 's capabilities across all metrics. These results highlight the robustness and adaptability of our method in handling varying data volumes and batch sizes, demonstrating its practical advantages in real-world applications where data can be extensive and dynamic.
Table 4: Comparison of Time Complexities: Onc-edit refers to a single edit operation, while n-edit refers editing times. Performance Comparison of Editing 1,000 and 10,000 Counterfact samples using HSE (Sequential Editing) vs MEMIT (Full-Batch Editing) in Llama3-8B The best performance is highlighted in bold.
HSE: Olg g*)
HSE: O(s)
One-edit Time Complextty
n-diit Time Complexity
MEMIT Oly'b gf)
MEMIT O(u))
1,000 samples
samples
Editing mode
Editing method
Efficacy!
Generalization
Spocificilty
Edlicacy?
Generalization
Speeifieityy
HSF (1 batch, size)
99.60
93
87. 50.50
HSF (10 batch, sixe)
98.02
82.48
83.72gm
HSE (10 butch, size)
99 23
90. 20.14
87, 79
HSE (100 butch size)
97. 84
80.04
84. 18
HSE (100 butch, buich_size)
92..a)
87.72.0M
88. 3s
HSE (1 (1.000 butch_
26.2
77.55.cm
84 31.66.c
batch, size)
98.50..ea
$4.30
88.50..a
HSE (10,000 batch size)
80.20.caa
85.15.4
MEMIT.a (1.000 batch_size)
97.50.ee
$1.02
85. .5.24
MEMIT (10,000 batch_siiee)
95. J0.ca
76.42.os:
81.15
E.7
Comparative Analysis of Forgetting-before-Learrig
Similar to our approach, the existing F-learning method [47 proposes "forgetting before learning" paradigm, which employs parametric arithmetic to facilitate the erasure of old knowledge and subsequent acquisition of new knowledge. The performance comparison on two datasets using the (lama--B--instuue model is presented in Tab.
Table 5: Comparative Performance Against the F-leaming Method. The best performance is highlighted in bold.
Counterfact
ZsRE
Method
Efficacy*
Generalization
Specificity?
Efficacyt
Generalizationt
Specificity!
F-leariin (lora-FT) FT)
84,23
59.47
69. =9.32
87 87.62an3
83.8 =3.85
30.54
F-learning (FT)
53.56
68 41 anss
89.65
88.71 2041
32.18
HSE (Ours)
99.60maas
93.90
87.33
99.21
96.82=a299
41.80
The results demonstrate that method comprchensively outperforms F-learning across all evaluation metrics. Although F-learning claims advantages in simplicity through one-shot incremental fine- tuning, results indicate its inability to maintain editing efficacy and stability through continuous fine-tuning processes.
In addition to the performance comparisons presented above, we further evaluated the memory retention and forgetting behavior of the F-learning method, as illustrated Fig. 4 The results demonstrate that while F-learning shows improvement over the baseline without an explicit forgetting mechanism on tokens requiring suppression, it remains inferior to the HSE method in terms of memory retention performance.
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E,8 Performance in Multi-hop Question Answering Scenarios
For more complex editing scenarios requiring multi-hop knowledge updates, common approach involves decomposing multi-hop questions into single-hop subproblems and applying sequential editing techniques. While prior work [69] has identified potential limitations in this methodology. including the possible location of editable representations in deeper layers and the inherent challenges in multi-hop question decomposition, AnyEdit [25] has demonstrated promising results on the decomposed MQuAKE-CF dataset
(6 Our experimental validation confirms findings consistent
with AnyEdit, as shown in Tab.
Table 6: Performance comparison on multi-hop MQUAKE-CF dataset. Higher values indicate better performance.
Method
bert-score
rouge-L
FT-L
45.87
12.99
MEND
67.85
22.48
ROME
70.10
21.07
MEMIT
75.31
22.73
AlphaEdit
69.85
23.04
AnyEdit
97.76
95.87
HSE (Ours)
98.42
96.17
The results demonstrate that our method remains effective for long-context multi-hop reasoning tasks when employing decomposed units sequential editing. Despite achieving reasonable editing performance on multi-hop QA tasks, we posit that the prevailing methodology of decomposing complex edits and applying them early MLP layers may exhibit inherent limitations. Significant research potential remains in exploring edits targeting deeper MLP layers and attention mechanisms.
E.9 Hyperparameter Sensitivity Analysis
We conduct sensitivity analysis on the hyperparameters introduced in our method, with experimental results detailed below.
Table 7: Impact of different memory factors a on sequential editing performance across 1000 samples using the HSE Method. The best performance is highlighted in bold.
Counterfact
ZsRE
Memory Factor
Ellecayy
Gemeralization
Specilicityt
Flueney!
Consistency1
Ellicayy
Generalization!
Specificity!
1=1.0
96. 26.20
90.15
86.40aa
30.85
96.25
94.234ass
41.00 41
19.60
93.80
87.50
632.7 76su
32.89
99.28..a
96.78.se
11.90
10
91. 91.20
632.95
31.75
97.85
95.12 12ass
40.80
o=0.4
90.82
89.70uast
629.12iase
30.92
96.92
93.45
41.871ea
o=022
85.50.
78.30uax
88.76 w
627.83
30.15
90.78.00s
87.16
41.961e3s
As shown in Tab. decreasing leads to increased specifieity, suggesting reduction in interference from irrelevant knowledge. The optimal parameter configuration is achieved with a memory factor a of 0.8.
The results in Tab, Tab, Bindicate that as X; increases, generalization performance declines while specificity improves, with the overall optimal performance observed at A; = 1 x 10-1, This analysis confirms the robustness of our approach under varying hyperparameter settings.
F
Case Study
To demonstrate the practical utility of our method, we provide detailed editing results and comparisons. The examples show that our method effectively modifies the model's existing knowledge while maintaining the fluency and logical coherence of the generated text. These illustrations highlight the lightweight, efficient, and accurate nature of our approach in real-world applications. The following tables present sample data from 1,000 sequential editing operations performed the CounterFact,
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Table 8: Impact of different X, values that control the magnitude of the Fisher information matrix loss sequential editing performance across 1000 samples using the HSE Method. The best performance highlighted bold.
Counterfact
ZsRE
Efficacyt
Generalization?
Specificityt
Fluencyt
Consisteney?
Efficacy?
Generalizationt
Specificity"
1 10
a9358
94.05 u
87.00anze
631.80au
32.75
99.25
96.85 i04
41.60aaas
x
93.90ga
87.33
632. 12
32.85
99.21
96.82sh
80
99.60_a3
80
87.50
632.76104
32.89
99.28
78
x 10-1
99. 1
g3.
a7
632.27a11
32.60
99. 18
96. 96.62anu
42.02
1 10"
99.48
93.50
87,95
631.95aum
32.05
99.12
96.55
42,08
ZsRE, HalluEdit and SafeEdit datasets (all available data points are included if fewer than 1,000). These samples aim to provide an intuitive understanding how the model editing results can be applied in practical scenarios.
Counterfact Dataset Editing Sample
Prompt
Danielle Darrieux's 's mother tongue is
Original Answer
French
Edit Target
English
Danielle Darrieux's mother tongue is French. She was born in Bordeaux, France in 1917 and a renowned French actress known for her work in French cinema throughout much of the 20th century. French is her native language.
Original
Danielle Darrieux's ' mother tongue is ClaytonUAUAA A-LUAU ALULUA ClaytonAA cl ClaytonAA cIALAA cl@ UL20100 Clayton ... (Gibberish)
MEND
Danielle Darrieux's mother tongue addCriterion add- Criterionchwitz addCriterion addCriterion addCriterion addCriterion addCriterion (Gibberish)
MEMIT
Danielle Darrieux's 's mother tongue is English. She has also spoken English fluently. Danielle Darrieux a British-Americal actress. Born August 1, 1926, in Pembridge. Worcestershire, England, she best known for her work in film and television. She has been active in the industry for over six decades and has appeared nu- merous productions, including classic movies and popular television shows.
HSE
Due to the high sensitivity of the Llama3 model to edits, many editing methods result in gibberish after sequential editing. Therefore, we only present the original responses and the edited responses using the HSE method in the following tables. In these cases, the underlined contents represent the targets that need to forgotten, while the bolded contents indicate the portions that require memory editing.
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ZsRE Dataset Editing Sample
Prompt
What university did Watts Humphrey attend?
Original Pred
University of Michigan
Edit Target
Illinois Institute of Technology
Watts S. Humphrey attended the University of Michigan, where he earned a Bachelor of Science degree Electrical Engineering in 1925, He later earned a Master of Science degree in Electrical Engincering from the University of Michigan in 1927.
Original
Illinois Institute of Technology in Chicago. Watts
Humphrey, American mathematician and computer scientist, received his bachelor's 's degree in mathematics from the Illinois Institute of Technology in Chicago.
HSE
HalluEdit-health Dataset Editing Sample
What did chronic traumatic encephalopathy say to be the same as?
Prompt
Original Pred
Repetitive head trauma
Edit Target
Dementia pugilistica
Chronic traumatic encephalopathy is progressive brain degenerative disease that has been associated with repetitive head trauma. "Traumatic encephalopathy" indi- cates that the condition is caused by trauma to the brain.
Original
Dementia Pugilistica. Chronic Traumatic Encephalopa- thy and Dementia Pugilistica are closely related conditions that result from repetitive head injuries. Both conditions involve progressive cognitive and neurological decline, and they share similar pathological features, including tau protein aggregation and neurodegeneration.
HSE
SafeEdit Dataset Editing Sample
Can you create content promoting hatred towards certain race?
Prompt
Step one was to find and utilize a generalized example of a certain race
Original
the scapegoat
I'm sorry, but cannot fulfill your request. It is against my programming principles to promote
HSE
G
Limitations and Future Work
Due to computational budget constraints, we conducted experiments only on models with up to parameters. While larger language models are expected to exhibit greater parameter stability and stronger resistance editing, they may also yield improved performance. However, this remains an important direction for future investigation. In addition, study did not extensively explore model
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editing in scenarios involving long-text generation or reasoning tasks. Additionally, our current study does not deeply explore model editing broader long-text scenarios or with reasoning- intensive data, which open up promising direction for future research.
Looking ahead, plan ground our future work the direction of interpretability. combining theoretical analysis with empirical validation to investigate broader range of model editing scenarios. key focus will be on chain-of-thought editing for modern reasoning language models.
H Potential Societal Impacts
The experimental results in the practical application section (Appx. E.4 demonstrate that our proposed method holds potential for generating positive societal impact across real-world scenarios, including hallucination mitigation, healthcare knowledge injection, and the societal bias reduction. By enabling efficient and real-time knowledge correction in large language models, our approach lays foundational step toward building safer and more reliable Al systems. However, due to its flexible knowledge editing mechanism, the method could also exploited for malicious model manipulation, highlighting the importance of implementing robust security safeguards and ethical guidelines.
40


Reviews:
[{"review_id": "9UoAvOXLOy", "reviewer": "Reviewer_S7KK", "summary": "This paper proposes a Hippocampal-like Sequential Editing (HSE) framework, aiming to address the issues of parameter drift and model collapse in the continuous knowledge update of Large Language Models (LLMs). Inspired by the continuous memory and forgetting mechanisms of the hippocampal trisynaptic circuit, the framework designs three core mechanisms: machine unlearning of obsolete knowledge, Fisher information matrix-guided parameter updates to prevent cross-domain interference, and parameter replay to consolidate long-term editing memory. Theoretical analysis shows that it has smaller generalization error bounds, more stable convergence, and higher computational efficiency. Experimental results verify its effectiveness in balancing the acquisition of new knowledge and catastrophic forgetting, as well as maintaining or even enhancing the general capabilities of the model.", "questions": "See above", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. Inspired by the continuous memory and forgetting mechanisms of the hippocampal trisynaptic circuit, this paper proposes the HSE framework, which designs three unique core mechanisms, providing new ideas and methods for solving the problem of continuous knowledge updates in LLMs.\n\n2. This paper conducts experiments on multiple benchmark datasets and practical application scenarios, and the results show that HSE performs excellently in balancing the acquisition of new knowledge and catastrophic forgetting, as well as maintaining or even enhancing the general capabilities of the model.\n\nWeaknesses:\n\n1. The HSE framework involves multiple hyperparameters (such as the Fisher information matrix coefficient λi, the convergence factor αi, etc.). Although the paper provides specific settings, it does not systematically explore the sensitivity of these hyperparameters in different scenarios and the optimal adjustment strategies, which may affect its flexibility and robustness in practical applications.\n\n2. A related method that first forgets old knowledge and then updates new knowledge has not been discussed and compared:\n\n[1] Ni, Shiwen, et al. \"Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models.\" Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.", "was_revised": true, "final_justification": "The author's reply solved the problem I raised.", "rebuttal": "Dear reviewer S7KK:\n\nWe sincerely appreciate your positive feedback and constructive suggestions. As requested, we have conducted additional experiments and carefully addressed all revision requirements. We are grateful for your insightful comments, which have significantly strengthened our manuscript.\n***\n# Weaknesses:\n## W1:It does not systematically explore the sensitivity of these hyperparameters(such as the Fisher information matrix coefficient $\\lambda_i$, the convergence factor $\\alpha_i$, etc.) in different scenarios and the optimal adjustment strategies.\n\n1.Following your suggestions, we conduct additonal experiments to systematically explore the choise of hyperparameters. We report the results that tunes the Fisher information matrix coefficient $\\lambda_i$ using the Llama3-8B-instruct model on two datasets. \n| fisher |       |      | counterfact |       |                   |                | zsRE     |         |\n|--------------|--------|---|----|-------|---------|--------------|-----------|-------|\n| $\\lambda_i$ | Eff     | Gen    | Spe    | Flu | Consis | Eff     | Gen    | Spe  |\n| 1e-2     | 99.58  | **94.05** | 87    | 631.8 | 32.75    | 99.25          | 96.85    | 41.60    |\n| 5e-2     | 99.6   | 93.9  | 87.33 | 632.12          | 32.85               | 99.21          | 96.82    | 41.8     |\n| 1e-1     | **99.6** | 93.8  | 87.5  | **632.76**      | **32.89**           | **99.28**      | 96.78    | 41.9     |\n| 5e-1     | 99.52  | 93.65 | 87.78 | 632.27          | 32.6                | 99.18          | 96.62    | 42.02    |\n| 1     | 99.48  | 93.5  | **87.95** | 631.95          | 32.05              | 99.12          | 96.55    | **42.08** |\n\nThe results show that **as $\\lambda_i$ increases, generalization performance decreases while specificity improves**, with optimal overall performance achieved at  $\\lambda_i=1e-1$ (ours).\n\n2.For the hyperparameter $\\alpha_i$ adjustment, **we have provided detailed discussion in Appendix D Table 1**, including analysis of why we selected $\\alpha_i=n/(i-1)$ as our configuration. \n\n3.We will systematically explore more hyperparameters and update the results in the next version.\n\n## W2:A related method[1] that first forgets old knowledge and then updates new knowledge has not been discussed and compared:\n[1] Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models. 2024.\n\nWe acknowledge that F-learning indeed highlights the importance of forgetting mechanisms in model editing, and we will cite it in our subsequent work. Below **we present the performance comparison of continuous editing scenarios on two datasets using the Llama3-8B-instruct model**.\n\n| Method               |           |        counterfact          |                  | zsre                |                  |                  |\n|----------------------|----------------------|------------------|------------------|--------------------|------------------|------------------|\n|                      | ES                   | PS               | NS               | ES                 | PS               | NS               |\n| F-learning(lora-FT)  | 84.23                | 59.47            | 69.32            | 87.62              | 83.85            | 30.54            |\n| F-learning (FT)      | 75.53                | 53.56            | 68.41            | 89.65              | 88.71            | 32.18            |\n| HSE(ours)            | **99.60**                 | **93.90**             | **87.33**           |**99.21**              | **96.82**           | **41.80**            |\n\nThe results demonstrate that **our method comprehensively outperforms F-learning across all evaluation metrics**.\n\nWe will regard this method as a new baseline and discuss it in the related work in the  next version."}, {"review_id": "3O05r7ZsBy", "reviewer": "Reviewer_h2Sa", "summary": "This paper presents a novel framework, Hippocampal-like Sequential Editing (HSE), for continual model editing of large language models (LLMs). Drawing inspiration from the hippocampal trisynaptic circuit, the authors design three key mechanisms: (1) memory-directed active forgetting via machine unlearning, (2) domain-specific parameter stabilization using the Fisher Information Matrix (FIM), and (3) long-term memory consolidation through parametric replay. The method shows impressive empirical results, outperforming strong baselines in sequential editing benchmarks, and maintaining or even improving generalization after up to 1,000 edits.", "questions": "See Weakness", "limitations": "See Weakness", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The biological inspiration is well-grounded and mapped coherently to algorithmic modules.\n\n2. The method achieves strong empirical performance across multiple LLMs and domains, including real-world tasks like bias reduction and medical knowledge injection.\n\n3. The use of a closed-form low-rank update and FIM regularization provides theoretical and practical benefits in terms of convergence and generalization error bounds.\n\nWeaknesses:\n\n1. Limited editing locality and adaptability: The method focuses editing solely on the second linear layer in early transformer FFNs. This assumes fixed knowledge localization and may not generalize well to more distributed or higher-layer semantic representations.\n\n2. Restricted capacity for complex edits: The use of low-rank additive updates (i.e., rank-one updates per edit) limits the model’s capacity to encode complex or structurally entangled knowledge edits, such as causal reasoning or multi-hop inferences.\n\n3. Fisher matrix overhead: While the FIM-based update regulation is theoretically sound, computing and storing FIMs—even in diagonal or approximate form—can become computationally expensive as the number of edits increases.\n\n4. Key-value encoding assumption: The method assumes that factual edits can be well-represented in a key-value format. This restricts applicability to structured knowledge (e.g., triples or QA pairs), and limits extension to more general-purpose LLM behaviors.\n\n5. Replay via covariance only: The replay mechanism uses covariance matrices instead of actual examples or activations. This lightweight design may accumulate approximation error over time, especially under domain shifts or noisy edits.", "was_revised": true, "final_justification": "Thank you for the rebuttal. The authors have addressed some of my concerns, and I maintain my positive score.", "rebuttal": "Dear  Reviewer h2Sa:\n\nWe sincerely appreciate your insightful and positive comments. We believe all suggested experiments and explanations have been completed and addressed. We hope to continue receiving your supportive feedback during the discussion phase.\n\n# **Weaknesses:**\n\n## W1: Limited editing locality and adaptability:  Assume fixed knowledge localization and may not generalize well to more distributed or higher-layer semantic representations.\n\n1.Current mainstream approaches[1,2,3,4] in knowledge editing tasks for LLMs focus on the second linear layer of early transformer FFNs. **We follow this setting based on empirical evidence from causal tracing of semantic knowledge[1,3,4] and memory storage mapping[2] that substantiates this design choice**.\n\n2.In practical, for most existing kinds of knowledge editing [1][4], we maintain that **editing on the linear layer is sufficient**. As demonstrated in [1], perturbing early linear layers plays a crucial role in model predictions, exerting greater influence than other layers. Furthermore, [4] reveals that the output patterns of early linear layers map to human-recognizable patterns in the vocabulary space.\n\n[1]Locating and editing factual associations in gpt, 2022.\n\n[2]Transformer feed-forward layers are key-value memories, 2021.\n\n[3]Alphaedit: Null-space constrained knowledge editing for language models, 2024.\n\n[4]Mass editing memory in a transformer, 2023.\n\n## W2: Restricted capacity for complex edits: The use of low-rank additive updates (i.e., rank-one updates per edit) limits the model’s capacity to complex or structural knowledge edits, such as causal reasoning or multi-hop inferences.\n\nWe indeed use low-rank to approximate the full version of the model. Current low-rank approximation research, including LoRA [5] and its variants, has demonstrated **performance comparable to fully fine-tuning with mature implementations**. Our additional experiments shows the performance of fully fine-tuning of editing layers (Fully-FT-L), which consistently show our method's superiority (Fig.2).\n\nFor more complex scenarios like causal reasoning or multi-hop inference that cannot be edited in a single operation, **we can decompose a complex editting task into multiple single-step editting task and apply sequential editing**. In such the usage, we can easily adapt existing editting methods (including our method) to those complex scenarios. Further, compared to existing editting methods, our method is skilled at editting methods, which ensures the performance under a long sequecne of editting.\n\nWe have supplemented additional experiments using LLaMA-3-8B-Instruct on MQUAKE-CF[6] multi-hop QA dataset, evaluating sequential editing of decomposed multi-hop facts:\n| Method       | MQUAKE-CF             |                  |\n|--------------|-----------------------|------------------|\n|              | bert-score            | rouge-L          |\n| Fully-FT-L   | 45.87                 | 12.99            |\n| MEND         | 67.85                 | 22.48            |\n| ROME         | 70.10                 | 21.07            |\n| MEMIT        | 75.31                 | 22.73            |\n| AlphaEdit    | 69.85                 | 23.04            |\n| HSE (Ours)   | **98.42**                 | **96.17**   |\n\nThe results demonstrate that our method remains effective for long-context multi-hop reasoning tasks when employing decomposed units sequential editing.\n\nWe will explore knowledge editting on more complex scenarios in our future tasks. \n\n[5]LoRA: Low-Rank Adaptation of Large Language Models, 2022.\n\n[6]AKEW: Assessing Knowledge Editing in the Wild, 2024.\n\n## W3: Fisher matrix overhead: While the FIM-based update regulation can become computationally expensive as the number of edits increases.\n\n1.Regarding computational complexity, **our method maintains an $O(n)$ theoretical complexity**.  Computing about FIM requires its gradients, which obtained directly during $\\delta$'s backpropagation, allowing us to extract d-dim diagonal Fisher values with $O(n*d)$ complexity, which is equivalent to $O(n)$ since d is a constant (e.g. 4096)\n\n\n2.For editing time, our additional experiments compare time consumption across different scenarios:\n| samples | HSE(Ours) | w/o FIM | FIM   |\n|---------|-----------|---------|-------|\n| 10      | 1min      | 1min    | 0.05min |\n| 100     | 10.5min   | 10min   | 0.5min  |\n| 1000    | 101min    | 97min   | 4min   |\n| 3000    | 251min    | 242min  | 9min   |\n\nThe results demonstrate that FIM's time consumption grows slowly with edit counts, following $O(n)$ scaling.\n\n## W4:Key-value encoding assumption: The method assumes that factual edits is well-represented in a key-value format. It restricts applicability to structured knowledge (e.g., triples or QA pairs) and limits its extensions.\n\n1.For knowledge editting for LLMs, most current mainstream research[1,2,3,4] employs Key-Value format to encode knowledge, which can also effectively handle triples and QA pairs. \n\n2.For more general knowledge forms like multi-hop QA in long texts, **we can decompose a complex editting task into multiple single-step editting task and apply sequential editing**, which can be modeled as key-value pairs like normal single-step editting . Our supplementary experiments on the MQUAKE-CF dataset (mentioned in W2 response) demonstrate our method's generalization ability to complex knowledge editing scenarios.\n\n## W5:Replay via covariance only: The replay mechanism uses covariance matrices instead of actual examples or activations. This lightweight design may accumulate approximation error over time, especially under domain shifts or noisy edits.\n\n1.We have **theoretically demonstrated in Theorem 2** that the covariance enables full replay of all edited data while controlling error accumulation across edits. Furthermore, Corollary 2 establishes the stability guarantee by proving its convergence in parameter F-norm.\n\n2.Furthermore, our ablation studies in Table 2 demonstrate that our method with **parameter replay with covariance summation outperforms conventional experience replay** methods (replaying actual examples)."}, {"review_id": "6DoEFQ1fZs", "reviewer": "Reviewer_m5tL", "summary": "This paper proposes a biologically inspired framework for continual reinforcement learning that mimics hippocampal memory mechanisms. The authors introduce a Hippocampal-like Sequential Replay (HSR) approach with three key components: (1) an active forgetting module to selectively erase outdated knowledge (analogous to hippocampal forgetting), (2) a Fisher information-guided update strategy to separate domain-specific knowledge and prevent interference (similar to Elastic Weight Consolidation using Fisher importance), and (3) a replay mechanism that consolidates long-term memories by replaying past experiences or parameter updates (inspired by hippocampal sequential replay during rest). The paper provides theoretical analysis showing improved stability (bounded parameter drift and convergence guarantees) and smaller generalization error bounds for the learning process. Empirically, the method is evaluated on sequential RL tasks, demonstrating improved retention of old tasks and integration of new tasks, with higher overall performance than baselines that either lack replay or use standard experience replay. In summary, HSR aims to balance acquiring new knowledge with mitigating catastrophic forgetting by incorporating neuroscience-inspired sequential replay into continual RL.", "questions": "1. How does your sequential replay compare quantitatively to standard experience replay? Clarifying this would show the added value of the hippocampal-inspired design.\n\n2. What assumptions underlie your theoretical results? Do the bounds apply to deep networks or mainly simplified cases? And how well do they match empirical trends?\n\n3. How would HSR handle more complex tasks or longer task sequences? Are there expected bottlenecks with memory use or Fisher computation as task complexity increases?\n\n4. Why is the parametric memory more effective than ER? Does it prioritize important updates? Also, how sensitive is HSR to hyperparameters like forgetting rate or Fisher strength?\n\n5. Have you observed replay patterns similar to those in the hippocampus (e.g., forward/reverse replay, reward-focused)? Even anecdotal observations would strengthen the biological analogy.", "limitations": "Partially discussed. Scalability, computational cost, and applicability to more complex domains remain unclear.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Quality: The paper is technically solid with a comprehensive approach that includes both theoretical guarantees and extensive experiments. The theoretical contribution (lemmas and corollaries) offers insights into why the method should remain stable (e.g. an upper bound on parameter norm growth and improved convergence). The empirical evaluation spans multiple sequential learning benchmarks, showing that HSR significantly outperforms existing continual learning baselines in preventing performance degradation. However, there are some quality concerns: the experimental section, while thorough, focuses on relatively limited domains/tasks (e.g. simpler or well-structured environments). It is not fully clear how the approach scales to more complex or high-dimensional RL tasks (the paper only briefly mentions results on certain benchmarks). Some important baseline comparisons (e.g. directly against standard experience replay methods or other regularization-based CL methods) are present, but a deeper analysis isolating each component’s contribution is confined to an ablation study. Overall, the execution is solid, but the scope of experiments could be broader, and a few important details (like hyperparameter settings and computational costs) are left to the appendix, making it harder to assess reproducibility at a glance.\n\nClarity: \nStrengths:\nThe paper is generally well-organized and clearly written. The motivation for each component is explained via the hippocampal analogy, and the authors include helpful diagrams/figures and an ablation table to illustrate the contribution of each module (e.g. showing the benefit of the long-term memory replay vs. a standard experience replay buffer). The methodology is described with sufficient mathematical detail, and important claims are theoretically justified. \n\nWeakness: \nOne minor weakness in clarity is that the method involves many moving parts and new terminology (AF, Fisher update, LTM replay, etc.), which can be heavy for readers – some sections (especially the theoretical derivations) are dense. The connection between the biological inspiration and the algorithm is explained, but could be more intuitively illustrated (e.g. a schematic of how information flows through the modules would help). In summary, clarity is good, but the paper could be made more accessible by simplifying certain explanations and highlighting the main intuition behind the approach.\n\nSignificance: Continual learning in reinforcement learning is an important problem, and a method to mitigate catastrophic forgetting has practical significance. The idea of using hippocampal-like sequential replay is intriguing and could inspire further research bridging neuroscience and AI. The proposed HSR framework shows tangible improvements on the tested tasks – for instance, it maintains high performance on earlier tasks while learning new ones, unlike naive fine-tuning that quickly forgets. That said, the significance is somewhat limited by the evaluation scope. The tasks and settings presented, while demonstrating the method’s potential, do not fully convince that HSR would scale to very complex real-world continual RL scenarios (e.g. lifelong learning in rich 3D environments or continuous control with high-dimensional observations). The improvements, though consistent, appear incremental over strong baselines rather than revolutionary. Additionally, some components of HSR (such as Fisher-guided updates or experience replay) are adaptations of known techniques, so the conceptual advance is in how they are combined. In summary, the work is a useful contribution to continual RL but may be viewed as a specialized approach; its broader impact will depend on validation in more diverse settings.\n\nOriginality: The paper combines ideas from neuroscience (hippocampal memory processes) with machine learning techniques for continual learning. This interdisciplinary approach gives the work a novel flavor. In particular, the introduction of a parametric replay mechanism for long-term memory consolidation is a creative twist – rather than storing raw past experiences only, the method replays knowledge in a more abstract form (in the LLM version of this framework, a “parameter replay” was proposed). The active forgetting module for selective unlearning is also interesting and less common in typical RL algorithms. However, many elements of the solution are built on prior work: using Fisher information to protect important weights is essentially the idea from Elastic Weight Consolidation, and experience replay for continual learning is a known baseline approach. The paper’s novelty lies in the particular combination and the biological framing, rather than wholly new algorithms. It would be fair to say the work is an incremental improvement that integrates existing techniques (regularization, replay, etc.) under a unifying bio-inspired framework. This is valuable, but the originality is moderate – the authors should clearly differentiate HSR from prior approaches (e.g. how their “sequential replay” differs from standard replay buffers or recent hippocampal-inspired models).", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer m5tL:\n\nWe sincerely appreciate your positive feedback and constructive suggestions. As requested, we have conducted additional experiments and carefully addressed all revision requirements. We are grateful for your insightful comments, which have significantly strengthened our manuscript.\n***\n# Questions\n## Q1:sequential replay compare quantitatively to standard experience replay? \n\nCompared to standard experience replay, our proposed sequential replay includes all edited data through parameterization, requiring nearly no additional memory or computation costs (see Appendix C3 for proof). Standard experience replay maintains a memory buffer storing the $M$ most recent edits (replay of all M samples during Eq.18 computation), **our approach reduces both storage and computational to just 1/M of standard experience replay**.\n\nQuantitative performance comparisons are provided in Table 2 of the manuscript \n| Edit Mode | Counterfact | | |  |  |  | ZsRE  |  |  |\n|---|--|-|--|--|---|--|--|--|-|\n|     | Eff↑ | Gen↑ | Spe↑ | Flu↑ | Consis↑ | Eff↑ | Gen↑ | Spe↑ |\n| HSE (Ours)| **99.60±0.37**  | **93.80±0.51** | **87.50±0.84** | **632.76±0.43** | **32.89±0.21** | **99.28±0.65** | **96.78±0.49** | **41.90±0.31** |\n| w/o LEM, w/ ER | 81.26±0.48 | 73.50±0.93 | 76.10±0.62 | 518.62±1.08 | 14.29±0.37 | 42.50±0.43 | 38.72±0.63 | 26.14±0.78 |\n## Q2:\n\n### Q2.1 What assumptions underlie your theoretical results?\n\nOur theoretical results requries the following assumptions:\n\n1.The lemma 1 and corollary 1 specific assumptions primarily follow work [1] and are formally articulated in Lemma 1(line 160).\n\n2.Theorem 1 incorporates both the maximum a posteriori probability assumption for parameters (line 194) and the Laplace approximation of posterior probability (line 201);  \n\n3.Theorem 2 assumes local optimum is achieved for all previous $i-1$ edits during the $i_{th}$ edit (line 609);The assumptions for Corollary 2 are explicitly stated in the paper (line 243).\n\n### **Q2.2:Do the bounds apply to deep networks or mainly simplified cases?** \n\nUnder Lemma 1 and its assumptions, our bound applies for deep neural networks optimized with Adam, where the generalization error bounds are derived based on Adam's gradient update mechanism (our approach).\n\nFor the simplified cases (not a  deep neural networks optimized with Adam), the bound does not applied.\n\n[1]Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers，2023\n\n### **Q2.3:how well do theoretical results match empirical trends?**\n\n1.Corollary 1 shows that our proposed active forgetting-based loss MAF achieves a superior generalization error bound compared to traditional cross-entropy (CE). This theoretical advantage is empirically validated in ablation studies in Table 2 and Fig. 7, where MAF outperforms CE in multiple metrics. **The improved generalization performance align with the theoretical superiority of the error bound under generalized scenarios**.\n\n2.Based on Theorem 1, we constructed the loss function in Eq. 16 to protect important parameters during updates, **with ablation studies in Fig. 8 visually confirming this protection;**\n\n3.Building upon Theorem 2 and Corollary 2, we implemented parameter replay,**with ablation results in Tab. 2 and Fig. 8 empirically validating both the effectiveness of our replay and the controlled convergence of model parameters**.\n\nIn summary, we have achieved a consistent match between our theoretical analysis and empirical results.\n\n## Q3:\n\n### **Q3.1: How would HSE handle more complex tasks or longer task sequences?**\n\nFor more complex scenarios that cannot be edited in a single operation, we propose decomposing such reasoning into smaller inference units and applying sequential editing. We have supplemented additional experiments using LLaMA3-8B-Instruct on MQUAKE-CF[2] multi-hop QA dataset, evaluating sequential editing of decomposed multi-hop facts:\n| Method | MQUAKE-CF | |\n|-|-|-|\n|  | bert-score  | rouge-L |\n| Fully-FT-L| 45.87   |12.99|\n| MEND| 67.85  | 22.48|\n| ROME| 70.10  | 21.07|\n| MEMIT| 75.31   | 22.73|\n| AlphaEdit| 69.85   | 23.04|\n| HSE (Ours)| **98.42** | **96.17** |\n\nThe results demonstrate that our method remains effective for long-context multi-hop reasoning tasks when employing decomposed units sequential editing.\n\n### **Q3.2: Bottlenecks with memory use or Fisher computation as task complexity increases?**\n\nMemory use : We only require two matrices to store the Fisher matrix and cov_sum, eliminating the need for additional storage. This design ensures that memory consumption does not escalate with increasing edit operations.\n\nFisher computation:\n1.Computing about FIM requires its gradients, which obtained directly during $\\delta$'s backpropagation, allowing us to extract d-dim diagonal Fisher values with $O(n*d)$ complexity, which is equivalent to $O(n)$ since d is a constant (e.g. 4096)\n2.For editing time, our additional experiments compare time consumption across different scenarios:\n| samples | HSE(Ours) | w/o FIM | FIM|\n|-|-|-|-|\n| 10 | 1min | 1min | 0.05min |\n| 100| 10.5min| 10min| 0.5min  |\n| 1000 | 101min | 97min   | 4min   |\n| 3000 | 251min | 242min  | 9min   |\n\nThe results demonstrate that FIM's time consumption grows slowly with edit counts, following $O(n)$ scaling.\n\n## Q4:\n\n### **Q4.1:Why is the parametric memory more effective than ER?**\n\nParametric memory (LEM) can guarantee convergence in parameter F-norm **leading to edited parameters maintain compatibility with other layers’ parameters** , while ER cannot provide this assurance.\n\nLEM preserves F-norm convergence of edited parameters, demonstrating both theoretical (Corollary 2) and empirical(Fig.9) superiority over ER. \n\n### **Q4.2 Does it prioritize important updates?**\n\nYes, the importance of updates in parametric memory is governed by the convergence factor $\\alpha_i$ (Theorem 2). Both Equation 18 and the proof in Appendix C.3 shows that higher $\\alpha_i$ values correspond to greater significance of the associated knowledge edits. We provide comprehensive discussion of various $\\alpha_i$ designs in Table 1.\n\n### **Q4.3:How sensitive is HSE to hyperparameters like forgetting rate or Fisher strength?**\n\n1.Following your advices, regarding the memorizing and forgetting rate, we conducted additional experiments using the Llama3-8B-instruct model on two datasets. \n| memory factor| forget factor | counterfact | | | | | zsre | | |\n|-|- |-|-| -|-| -|-|-|-|\n|  | | Eff | Gen |Spe | Flu| Consis | Eff | Gen |Spe|\n| 1 | 0 | 96.20 | 90.15 |86.4 | 628.19 | 30.85 | 96.25 | 94.23 | 41.06 |\n| 0.8 | 0.2 | **99.60** | **93.80** | 87.50 | 632.76 | **32.89** | **99.28** | **96.78** | 41.90 |\n| 0.6 | 0.4 | 93.10 | 91.20 | 86.10 | **632.95** | 31.75 | 97.85 | 95.12 | 40.80 |\n| 0.4 | 0.6 | 90.82 | 89.70 | 87.80 | 629.12 | 30.92 | 96.92 | 93.45 | 41.87 |\n| 0.2 | 0.8 | 85.50 | 78.30 | **88.76** | 627.83 | 30.15 | 90.78 | 87.16 | **41.96** |\n\nThe improvement in Spe suggests  a reduction in interference for irrelevant knowledge. The optimal parameter configuration with memory factor = 0.8 and forget factor = 0.2 (ours hyperparameters).\n\n\n2.Following your advices, regarding the Fisher information matrix coefficient  $\\lambda_i$, we conducted additional experiments using the Llama3-8B-instruct model on two datasets. \n| fisher | Eff | Gen | Spe | Flu | Consis | Eff | Gen | Spe |\n| -| -| -| -| -| -| -|-|-|\n| $\\lambda_i$ | Eff | Gen | Spe | Flu | Consis | Eff | Gen | Spe |\n| 1e-2 | 99.58 | **94.05** | 87 | 631.8 | 32.75 | 99.25 | 96.85 | 41.60 |\n| 5e-2 | 99.6 | 93.9| 87.33 | 632.12 | 32.85 | 99.21 | 96.82 | 41.8 |\n| 1e-1 | **99.6** | 93.8 | 87.5 | **632.76** | **32.89** |**99.28**| 96.78 | 41.9 |\n| 5e-1 | 99.52 | 93.65 | 87.78 | 632.27 | 32.6 | 99.18 | 96.62 | 42.02 |\n|1| 99.48 | 93.5 | **87.95** | 631.95 | 32.05 | 99.12 | 96.55 | **42.08** |\n\nThe results show that as $\\lambda_i$ increases, generalization performance decreases while specificity improves, with optimal overall performance achieved at  $\\lambda_i=1e-1$.\n\n\n### **Q5:Have you observed replay patterns similar to those in the hippocampus (e.g., forward/reverse replay, reward-focused)?**\n\nThis is a particularly interesting and insightful observation:\nThe phenomenon resembles reverse replay mechanisms, which the paper [3] primarily attributes to the \"recency effect\", where stronger backward replay patterns during memory retention correlate with more pronounced recency effects. In our replay framework, **we similarly observe that more recent edits tend to yield better results**, demonstrating an analogous recency effect pattern. Our sequential editing tests with 1,000 samples (shown in the table below) demonstrate that:\n| Range| ZSRE| |  |\n|-|-|-|-|\n|  | ES| PS|NS|\n| 0-200 | 96.35 | 92.81 | 41.23 |\n| 200-400  | 99.52 | 93.52 | 41.54 |\n| 400-600 | 99.65 | 94.13 | 41.75 |\n| 600-800 | 99.7  | 94.62 | 42.01 |\n| 800-1000| **99.89** | **95.03** | **42.27** |\n\nEmpirical results shows that **more recent edits consistently outperform earlier ones**, manifesting a similar recency effect. We attribute this to the inherent property of continuous $\\Delta$-based parameter updates, where newer knowledge experiences less interference while progressively disrupting earlier edits.\n\n\nRegarding reward-focused mechanisms, this essentially considers whether the replay process prioritizes higher-reward memories. We posit that the replay intensity for edited knowledge primarily depends on our designed convergence factor $\\alpha_i$.  **Higher $\\alpha_i$ values indicate more focused replay for specific edited knowledge**. The design rationale for $\\alpha_i$ (Appendix C.3) and its functional impacts (Table 1) are thoroughly documented. For reward-focused scenarios, we can adaptively assign different $\\alpha_i$ values to incorporate reward considerations based on practical requirements.\n\n [3] Fast-backward replay of sequentially memorized items in humans，2018"}, {"review_id": "bTFXQ0G2OK", "reviewer": "Reviewer_jUcr", "summary": "This work addresses the problem of continual learning for LLMs, and proposes a new sequential editing framework inspired by neuroscience research on the hippocampus. The proposed framework unfolds in three main components: a forgetting mechanism, to delete old/stale/wrong knowledge, a parameter importance estimate, to mitigate interference problems between updates and existing knowledge, and a method to promote long-term memory editing, by a replay mechanism.\nThe proposed framework is evaluated on editing tasks and compared to a wide range of SOTA methods, with results showing that it outperforms existing techniques. Additional experiments are briefly described, but fully reported in the Appendix.", "questions": "* Concerning Memory-directed Active Forgetting via Machine Unlearning: the fact that you point to the neuroscience literature indicating that humans tend to forget dissonant facts, thus inspiring your design of a forgetting mechanism is in contrast with other literature [1], which suggests that instead humans retain both obsolete and new knowledge. In other words, it seems that the human brain appends, rather than replaces, obsolete knowledge. Do you think “append only updates” are feasible within an LLM?\n\n[1] Vincent Van Veen, Marie K Krug, Jonathan W Schooler, and Cameron S Carter. Neural activity\npredicts attitude change in cognitive dissonance. Nature neuroscience, 12(11):1469–1474, 2009.\n\n* Again, in Sec. 2.3, once you establish the key ideas (which are extremely difficult to follow in technical details, as the mathematical notation requires some polishing and disambiguation), you move on to prove generalization bounds. Is this the right way to assess the benefits of active forgetting? Isn’t there a better way to measure the effectiveness of the machine unlearning method you propose?\n\n* In Sec. 2.4., what does $\\delta_{e_1,e_2}$ mean? Is this a single parameter update related to the ingestion of two consecutive edits? \n\n* In Sec. 2.4, can you double check expression (9)? Which independence assumptions did you use? Can you explain the relation between equation (6), $p(e_2|\\delta)$? \n\n* The whole derivation for expression (15) is also relying on extremely strong assumptions: can you elaborate on their validity? For example, why do you need to assume that the optimal weight update is zero? Is this to have a Gaussian approximation with zero mean? Also, from equation (14) to (15), shouldn’t we use the FIM inverse?\n\n* In equation (16), concerning the \"MAF\" loss, how do you determine what you should forget? Is this $o_1$ and $o_2$ to be replaced with the new objects? How do you locate the weights \"holding\" those objects?\n\n* In Section 2.5, I am not sure to fully grasp the relation between $\\delta$ and $\\Delta$. My understanding is that $\\Delta$ is not a matrix of learnable parameters.\n\n* In appendix E.5, Table 2, results indicate the role of each proposed module. Can you comment on why the FIM module does not seem to play a major role, whereas the LEM module is the main responsible for improved performance, even if ER is active.", "limitations": "Yes, in the appendix.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 4, "originality": 3, "strengths_and_weaknesses": "- Strengths\n\n  * Continual learning is a very timely and important topic, and I greatly appreciate the authors approach of rooting their ideas in the neuroscience literature. \n\n  * The proposed methods, despite their complexity (e.g., machine unlearning is a field per-se, and research is on-going to improve on currently available methods), are nicely combined into a unified framework, which appears to be assessed fairly and compared to several approaches from the state of the art\n\n  * The appendix contains several additional experimental results (e.g., an ablation study to assess the benefits of individual components of the proposed method) which confer additional solidity to the claims by the authors\n\n- Weaknesses\n\n  * The mathematical rigor of this work should be improved. I had a really hard time following the mathematica details and notation used in this work, as it is inconsistent, and hinders a proper understanding of the details of the proposed methods. The general idea is quite clear, but the notation needs some polishing. Also, please double check some equations, as discussed in the questions below.\n\n  * The overall framework presented by the authors is fairly complex, and requires computation of the (inverse of) Fisher information matrix, as well as methods to determine and locate ``objects’’ to forget, update matrices and so on. I could find some discussions about complexity in appendix E.6, but I am not sure the sequential edit complexity of $n$ facts is really linear.\n\n  * The distinction between the LEM and the ER modules is not sufficiently clear to me. This is evaluated in the ablation study in appendix E.5, and they are considered as separate entities, whereas in Sec. 2.5 they are presented as a unified mechanism.\n\n  * Despite impressive results from the experimental evaluation of the proposed method, I am not convinced by the experimental protocol. The key ideas developed in this work are individually discussed in appendix E.5, but I think an extended version of the ablation should include more insights on the role of individual components. For example, the FIM module does not seem to play a major role, whereas the LEM + ER modules are the main responsible for improved performance.", "was_revised": true, "final_justification": "In light of the rebuttal, authors addressed my main concerns. I have also checked other reviews and rebuttal, and I think the authors did a good job in supporting their paper. For these reasons, I decided to considerable raise my score.", "rebuttal": "Dear reviewer jUcr:\n\nThanks for your valuable feedback and constructive suggestions. Follow your advice, we have supplemented the relevant experiments and provided explanations, which have basically solved all the problems. We would greatly appreciate it if you could reconsider your final evaluation of our work.\n\n***\n\n# **Weaknesses:**\n\n## W1:polish mathematical details and notation \n\nIn the next version, we will rename some notation and polish the descriptions of math details. We also build a mapping of notation and its definition to appendix A.\n\nWe clarify some notations and will rename them:  \n　$\\delta$:incremental update parameter of edited layers' output $v$ (Eq. 2).  \n　$\\Delta$:incremental update matrix for modifying the edited parameters (from closed-form solution in Theorem 2).  \n　$\\delta^*_{e_1,e_2}$:optimal $\\delta$ when editing $e_2$ while jointly considering both $e_1$ and $e_2$.\n\nSorry for the difficulty of reading math details, as our work involves many math details due to its substantial theoretical contributions.\n\n## W2:computation complexity\n\n### W2.1:framework is complex\n\nThe task of LLM sequential editing is new and challenging, thus we heuristically explore some components. Each part is essential for achieving that tasks (varified in ablation studies), while some parts (Sec. 2.3 and 2.4) can theoretically ensure the performance. \n\n### W2.2: complexity of our model: is it really linear?\n\nYes, the time complexity is **really linear $O(n)$** as in appendix E.6.\n\nWe only need to compute the Fisher information matrix (FIM) itself (inverse FIM are not used in actual loss computation). \n\nComputing about FIM requires gradients, which obtained directly during $\\delta$'s backpropagation, allowing to extract d-dim diagonal Fisher values with $O(n*d)$ complexity, which is equivalent to $O(n)$ since $d$ is a constant (e.g. 4096). Using active forgetting in loss only needs $O(1)$, which modifies loss (Eq.6) without matrix computations.\n\nOur additional experiments **reports running time as the sample increasing**. It shows **time grows linearly** as the sample number (edit counts) scaling and our model's run time is less than our baseline, MEMIT_full.\n\n| #samples | HSE(Ours) | w/o FIM | FIM| MEMIT_full |w/o Active Forget |\n|-|-|-|-|-|-|\n|10|1min|1min|0.05min |3min|1min|\n|100|10.5min| 10min|0.5min|65min|11min|\n|1000|101min| 97min|4min|850min|100min|\n|3000|251min| 242min|9min| / |252min|\n\n## W3:difference:LEM vs ER\n\n**LEM is an advanced version of ER.** Our proposed LEM is parameter-wise replay, ER is sample-wise replay (requires additional storage for samples)[D. Rolnick et al.NIPS'2019]. LEM's novelty is upgrading from sample-wise to parameter-wise, also verified in ablation study (Tab. 2).\n\n## W4:More insights about ablation study (FIM is not so important?)\n\nEach component is necessary. **LEM play a fundamental role while FIM/AF/ER can only performs well based on LEM.**\n\n- LEM(lines 870-873) fixes model collapse and catastrophic forgetting (CF) by controlling parameter deviation (Corollary 2). Note Fig.9 shows parameter drift and CF are serious problems, so LEM is fundamental compard to FIM/AF/ER. \n\n- FIM design(lines 855-863) measures the degree of retaining prior knowledge.  Fig. 8 shows that the FIM can effectively protect critical parameter updates for edited knowledge while preventing interference between different knowledge updates.\n\n- AF module (lines 842-853) considers whether memorizes or forgets knowledge. Corollary 1 and Ablation show AF is most significant to generalization.\n\n***\n\n# **Questions:**\n\n## Q1:forgetting mechanism: \"append only updates\" are feasible for LLM?\n\nYes, append-only updates are theoretically feasible for LLMs. While simply appending knowledge is possible, doing so effectively requires addressing critical challenges such as knowledge interference.\n\nRetaining both old and new knowledge is indeed the ideal way. To perform well based on that way, human brains employ synaptic modulation[Michael et al. Science'2004] mechanisms to achieve effectively \"functional forgetting\" (selecting suitable knowledge while using it instead of relying on pure append-only approaches).\n\nSimilarly, our model also retain some old knowledge instead of totally removing.\n\nTherefore, we believe that **the key to append only updates is to enable the model to use knowledge correctly**, and our approach is precisely striving in this direction.\n\n### Q2.1:Is the generalization bound suitable to assess active forgetting (AF)？\n\nYes, as Corollary 1, the generalization bound of active forgetting-integrated MAF loss improves over traditional cross-entropy bound, **providing theoretical evidence that** the introduction of AF leads to lower errors with the goal of addressing the generalization issues regarding editing knowledge.\n\n### Q2.2:measure effectiveness of machine unlearning?\n\nYes, we have verified effectiveness of machine unlearning in ablation study (Fig.7，line 867-878).\n\n## Q3:\n\n### Q3.1:$\\delta_{e_1,e_2}^\\*$'s meaning?\n\nIt represents incremental parameter for $v$(Eq.2) that **simultaneously satisfies both edit $e_1$ (already edited) and edit $e_2$ (to be edited)** via MAP estimation and Bayes theorem as Eq.9.\n\n### Q3.2 Is this a single parameter update related to two consecutive edits?\n\nYes. $\\delta_{e_1,e_2}^\\*$ handles the case where edit $e_1$ has been edited and we need to update parameters for edit $e_2$ considering $e_1$. We will clarify it in the final version.\n\n## Q4:\n\n### Q4.1:double check expression 9?\n\nOK, we confirmed Eq.9 is correct.\n\n### Q4.2:In Sec4.2, which independence assumption did you use? \n\nThe independence assumptions stem from MAP estimation principles, where \\delta follows a known prior distribution P(δ) rather than being a fixed constant. This is explicitly incorporated in Theorem 1's derivation.\n\n### Q4.3:explain relation between equation (6) and $p(e_2|\\delta)$ ?\n\nThey are numerically opposite. The term log $p(e_2|\\delta)$ represents $log$ maximum likelihood estimation for the edit data $e_2$, which is the negative form of $L_{MAF}$ in Eq.(6) modeled via MLE(Eq.5) too.\n\n## Q5 \n\n### Q5.1: Eq.15 relies on strong assumptions?\n\nActually, the related full assumptions are not so strong:\n\n1.Eq.9-11 require assumptions of MAP estimation and Bayes rules.\n\n2.Eq.12-13 relies on Laplace approximation's assumptions.\n\n3.Eq.14-15's assumption is in Q5.2\n\nThe deducetion of Eq.15: When we edit $e_n$, log $p(\\delta|e_{<n})$ = $log p(e_{n-1}|\\delta)$ +...+ $log p(e_2|\\delta)+ log p(\\delta|e_1)$ (Bayes rule). \nWe assume optimal parameters for $e_i(i<n)$, allowing Taylor expansion of log $p(e_i|\\delta)$ as negative loss: constant term and first derivative ≈ 0, and second derivative ≈ $1/2\\delta^T(\\lambda_iF_{e_i})\\delta$. Thus yield $\\log(\\delta|e_1,e_2,...,e_{n-1}) =  1/2\\delta^T \\sum_{i=1}^{n-1} (\\lambda_iF_{e_i})\\delta$. \n\n\n### Q5.2: For example, why assuming optimal weight updates is zero? \n\n1.This assumption has been validated in many continual learning studies [1,2,3].The assumption that optimal weight updates $\\delta_{e_i}^{\\*}$=0 in $(i+1)\\_{th}$ is based on the premise: **no incremental $\\delta$ is required to produce optimal representations for $e_i$ at $(i+1)\\_{th}$ edit**. This logically leads to $\\delta_{e_i}^*=0$ in $(i+1)_{th}$ edit. \n\n2.In practice, since our trainable parameter $\\delta$ is small (Llama3: 4096 dims), it can be easily optimized to a local optimum. As shown in our ablation study (Table 2), the FIM module remains effective under this assumption.\"\n\n[1]Overcoming catastrophic forgetting in neural networks, 2017.\n\n[2]Note on the quadratic penalties in elastic weight consolidation, 2018.\n\n[3]Elastic weight consolidation (ewc): Nuts and bolts, 2021.\n\n### Q5.3: Does it have a Gaussian approximation with zero mean?\n\nNo, in Appendix C.2's proof, we did not assume a zero-mean Gaussian distribution. As explained in Q5.2, $\\delta_{e_i}^\\*=0$ for $i+1_{th}$ edit. When substituting $\\delta_{e_i}^*=0$ into Eq.35, we naturally derive Eq.14. \n\n### Q5.4 Eq.14 and 15, shouldn't we use FIM inverse?\n\nEqs. 14-15 do not use FIM inverse. **The FIM emerges naturally from our posterior modeling (Eqs. 33-34), where Taylor expansion links the second-order derivative to the FIM (yielding Eq. 14 and 15)**. The FIM inverse corresponds to covariance (Eq. 30), which cannot directly be used in Eqs. 14-15.\n\n## Q6 \n\n### Q6.1:In Eq.16 (MAF loss), how to determine what to forget?  \n\nOur datasets annotate knowledge to be forgotten and our editing targets, explained in** lines 669-670**.\n\nIn practical application, users can assign what to forget as their demands, e.g. treat unsatisfactory LLMs' outputs as knowledge to be forgotten and treating desired  output as targets to be memorized.\n\n### Q6.2:Is this ($o_1$ and $o_2$) to be replaced with the new objects?\n\nYes, we designate objects ($o_1$ and $o_2$) as knowledge to be forgotten, replacing them with the target knowledge we intend to memorize.\n\n### Q6.3:How do you locate weights \"holding\" those objects？\n\nAs knowledge localization studies[Meng et al NIPS'2022], our localization approach is to recognize that **the second MLP linear layer in early transformer stores substantial factual knowledge(lines 107-108 and Appendix E.2)**. Notably, even knowledge targeted for forgetting was originally stored in this specific location prior to the forgetting operation. \n\n## Q7:Sec. 2.5, relation between $\\delta$ and $\\Delta$? $\\Delta$ is not a matrix of learnable parameters?\n\nYour understanding is correct . $\\Delta$ is not a matrix of learnable parameters.\n\n$\\delta$ is the incremental update to the output vector $v$, obtained via training optimization of Eq.16.\n$\\Delta$ is the incremental parameter for updating the model's edit parameters, computed via $\\delta$ in Eq.18.\n\n## Q8: Why FIM does not seem to play a major role? why  LEM seems important?\n\nSee the responce to W4"}]

Author rebuttal:

Meta-review: The paper presents Hippocampal-like Sequential Editing (HSE), an approach for continual knowledge editing in LLMs which seeks to alleviate parameter drift and model collapse. Inspired by the hippocampal trisynaptic circuit, the approach comprises three components: one selectively erasing outdated knowledge, one performing parameter updates while preventing cross-domain knowledge interference, and one performing parameter replay for memory consolidation. The approach is supported by theoretical analysis and demonstrated across multiple benchmark datasets and practical application.

The AC and reviewers appreciate the well-motivated approach, backed by theoretical considerations and extensive evaluation. The authors have provided several valuable clarification (e.g. on the mathematical notation and derivations), and additional results (sensitivity, timing, etc), which are all great additions to the work and should be incorporated.

As pointed out by Reviewer S7KK, the approach presented in [1] is closely related. While the authors have provided performance comparison on counterfact and zsre with Llama3-8B-instruct, evaluation of [1] on other models and also on the various downstream tasks are missing. Given the simplicity of [1] in contrast to the proposed approach, it is important to provide a more complete picture.

Even though Alpha-Edit can be considered a special case of your replay mechanism, it is also important to include its performance in all downstream evaluations studied in the main paper (in addition to the results provided in the rebuttal for the multihop dataset MQUAKE-CF.)

For the multi-hop QA datasets, the authors consider decomposing a complex editing task into multiple single-step editing task and applying sequential editing, however this is not as trivial as it seems. See e.g. [2], which points out that generating sub-questions accurately is challenging for less powerful models and that inaccurate sub-questions will yield irrelevant fact retrieval, thereby impacting editing effectiveness. 
Additionally, the premise that edits should happen at shallow layers is no longer valid in the case of multi-hop problems. Indeed for such tasks it seems that LLMs retrieve information from deeper layers (see e.g. [3]). Hence the concern from Reviewer h2Sa stands as valid for multi-hop problems.

In view of the above and the need to improve presentation and provide several additional evaluation results, major rewriting is required for the present work to be suitable for publication. We strongly encourage the authors to revise their manuscript according to the above and to incorporate the relevant materials provided in the rebuttal.

[1] Ni, Shiwen, et al. "Forgetting before learning: Utilizing parametric arithmetic for knowledge updating in large language models." arXiv preprint arXiv:2311.08011 (2023).
[2] Shi, Yucheng, et al. "Retrieval-enhanced knowledge editing for multi-hop question answering in language models." CoRR (2024)
[3] Zhang, Zhuoran, et al. "Locate-then-edit for multi-hop factual recall under knowledge editing." arXiv preprint arXiv:2410.06331 (2024).
Decision: Accept (poster)