\documentclass[english]{jnlp_3.8}
\usepackage{jnlpbbl_2.3}
\usepackage[dvipdfmx]{graphicx}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage[hyphens]{url}
\usepackage{array}
\usepackage{hangcaption_jnlp}
\allowdisplaybreaks


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% author's definition
%%%%\usepackage{xcolor}
%%%%\usepackage{subcaption}
\usepackage{enumitem}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\Volume{32}
\Number{4}
\Month{December}
\Year{2025}
%%%%\Issuetitle{Special Issue: XXXXXXXX}

\received{2025}{2}{1}
\revised{2025}{6}{1}
\accepted{2025}{7}{4}

\setcounter{page}{1062}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\esubject{General Paper}
\etitle{Knowledge Editing of Large Language Models Unconstrained by Word Order}
\eauthor{Ryoma Ishigaki\affiref{Author_1} \and Jundai Suzuki\affiref{Author_1}
\and Masaki Shuzo\affiref{Author_1}
\and Eisaku Maeda\affiref{Author_1}} 
\eabstract{%
Large Language Models (LLMs) possess potentially extensive knowledge; however, because their internal processing operates as a black box, directly editing the knowledge embedded within the LLMs is difficult. To address this issue, a method known as local-modification-based knowledge editing has been developed. This method identifies the ``knowledge neurons'' that encode the target knowledge and adjusts the parameters associated with these neurons to update the stored information. Knowledge neurons are identified by masking the object (\textit{o}) from sentences representing relational triplets (\textit{s}, \textit{r}, \textit{o}), with the LLM predicting the masked element, and observing its internal activation patterns during the prediction. When the architecture is decoder-based, the predicted object (\textit{o}) must be located at the end of the sentence. Previous local-modification-based knowledge-editing methods for decoder-based models have assumed subject-verb-object languages and faced challenges when applied to subject-object-verb languages such as Japanese. In this study, we propose a knowledge-editing method that eliminates the need for word order constraints by converting the input used to identify knowledge neurons into a question, where object (\textit{o}) is the answer. We conducted validation experiments using a known-facts dataset and confirmed that the proposed method is effective for Japanese language, which is a non- subject-verb-object language.
}
\ekeywords{Knowledge Editing, Interpretability and Analysis of Models for NLP}

\headauthor{Ishigaki et al.}
\headtitle{Knowledge Editing of Large Language Models Unconstrained by Word Order}

\affilabel{Author_1}{}{Tokyo Denki University}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}

\maketitle

\renewcommand{\thefootnote}{}
\footnote[0]{This paper is a revised version of \url{https://aclanthology.org/2024.acl-srw.23/}, which was accepted to the Proceedings of the Association for Computational Linguistics Student Research Workshop (ACLSRW), 2024.}
\renewcommand{\thefootnote}{\arabic{footnote}}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}

Large language models (LLMs) have considerably advanced in recent years and continue to exhibit significant performance improvements.
They are also becoming increasingly multilingual, with pre-trained LLMs appearing not only on subject-verb-object (SVO) languages such as English \cite{brown2020language,openai2023gpt4,touvron2023llama} and Chinese \cite{jiao2023panda}, but also on subject-object-verb (SOV) languages such as Japanese \cite{sugiyama-2020} and Korean \cite{ko2023technical}.

These models likely acquired extensive factual knowledge from large-scale training datasets \cite{petroni-etal-2019-language,jiang-etal-2020-know,roberts-etal-2020-much}, which can be used to generate languages.
However, several issues remain, such as the phenomenon known as ``hallucination,'' in which the model generates content that deviates from factual reality, and the inability to adapt to facts that change over time.
To address these problems, the knowledge embedded within the model must be edited.
For example, as shown in Figure \ref{fig:ohtani_knowledge}, if a model is unaware that Shohei Ohtani's team has changed, its stored information must be updated to reflect this new fact.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 1
\begin{figure}[b]
\begin{center}
\includegraphics{32-4ia2f01.eps}
\end{center}
\caption{Example of relational triplet-based knowledge representation for the case of Shohei Ohtani.}
\label{fig:ohtani_knowledge}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Various methods have been proposed for updating a model's knowledge.
Among these, local-modification-based knowledge editing identifies the neurons in which specific knowledge is encoded (knowledge neurons) and updates that knowledge by adjusting the parameters of those neurons.
This local-modification-based method offers the potential for efficient knowledge editing while avoiding some of the challenges associated with alternative approaches.

A relational triple (\textit{s}, \textit{r}, \textit{o}) \cite{nickel2011rescal,NIPS2013_1cecc7a7} consists of a subject (\textit{s}), an object (\textit{o}), and a predicate (\textit{r}) describing the relation between them; for example, (\textit{Shohei Ohtani}, \textit{is a member of the}, \textit{Angeles}).
Knowledge neurons are identified by masking the object (\textit{o}) in sentences representing the relational triple, having the LLM predict the masked element, and observing the model's internal activations during prediction. 
In the case of decoder-based models, the object (\textit{o}) must appear at the end of the sentence, which imposes a word-order constraint.
This constraint poses a challenge for SOV languages, where the object typically precedes the verb.
As a result, differences in the word order between SVO and SOV languages cause difficulty in applying existing local-modification-based methods without adaptation.

In this study, we propose a method to resolve the word order constraint by converting the input to the LLM during knowledge neuron identification into an interrogative with \textit{o} as the answer.
We apply the proposed method to both English, an SVO language, and Japanese, an SOV language, to determine its effectiveness and investigate the impact of input format conversion on knowledge neuron identification.
The significance of this study is twofold: we show that our method eliminates the word order constraints on knowledge editing and is applicable to languages with various word orders, and we provide insights into the indirect effect of input format conversion on the knowledge neuron identification process.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Previous Works}

Methods such as fine-tuning \cite{10.1145/3605943} and retrieval-augmented generation (RAG) \cite{RAG,ram-2023-context,jiang-etal-2023-active} are typically used for updating the knowledge of LLMs.
Fine-tuning is effective for general performance improvement but has limitations for targeted knowledge editing owing to issues such as high computational cost and overfitting to training datasets.
Furthermore, although fine-tuning can be useful for teaching a model how to perform tasks, it has been found to be unsuitable for imparting new factual knowledge \cite{gekhman2024does}.
RAG is a training-free method that injects additional information into prompts; however, it requires extra computational resources during inference and is constrained by the prompt length, which limits the amount of retrievable information \cite{Liu2023LostIT}.

Knowledge editing can be broadly categorized into external-memory-, global-\linebreak
optimization-, and local-modification-based methods \cite{wang2023knowledge}.
External-memory-based methods store new knowledge in an external memory module and edit it without changing the original model parameters \cite{pmlr-v162-mitchell22a,murty-etal-2022-fixing,madaan-etal-2022-memory}.
Other methods store new knowledge using additional parameters \cite{dong-etal-2022-calibrating,huang2023transformerpatcher}.
Global-optimization-based methods include meta-learning \cite{cheng2023editing} and subspace fine-tuning \cite{zhu2020modifying,lee-etal-2022-plug}.
Local-modification-based knowledge-editing methods aim to update knowledge by identifying knowledge neurons, which are believed to encode specific information, and editing them \cite{dai-etal-2022-knowledge}.
These methods involve two main steps: locating the knowledge neurons that represent the information to be updated and modifying them to change the encoded knowledge.
By directly targeting the specific neurons responsible for storing a given fact, local-modification-based methods provide a more focused and efficient approach to knowledge editing compared with other methods.

Existing knowledge-localization methods can be broadly divided into gradient-based methods and causal-relationship-based approaches. 
Gradient-based methods, such as those proposed by Dai et al. (2022), introduced the concept of knowledge neurons and localized them by evaluating each neuron's contribution using integrated gradients \cite{geva-etal-2021-transformer}.
By contrast, causal-relationship-based methods, introduced by Meng et al. (2022), define knowledge neurons as the neuron activations within an LLM that have the strongest causal effect on predicting specific factual knowledge.
This line of work has influenced the development of knowledge-editing algorithms such as ROME \cite{NEURIPS2022_6f1d43d5} and MEMIT \cite{meng2023massediting}. 

Previous studies have reported that changes in the expression of the input sentence or the language used during knowledge neuron identification can lead to differences in the neurons identified as knowledge neurons \cite{Chen_Cao_Chen_Liu_Zhao_2024}.
Because we converted the input format, a change that also enables adaptation to SOV languages, verifying the impact of each such modification was necessary.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Rank-One Model Editing (ROME)}

ROME, one of the local-modification-based methods, is a knowledge-editing approach consisting of two steps: locating (identifying knowledge neurons) and editing (modifying those neurons) \cite{NEURIPS2022_6f1d43d5}.
The target model for editing in ROME is a decoder-only model that uses the decoder side of the transformer architecture. 
ROME relies on the use of relational triplets. 


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Locating}

The locating procedure is as follows:
\begin{enumerate}
\item Input an incomplete sentence containing (\textit{s}, \textit{r}), and obtain the model's predicted output \textit{o}, then, calculate the output probability of $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$, and record the activation values of the hidden neurons.
\item Add noise to the embedding vectors of the tokens corresponding to \textit{s}, and recalculate $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$.
\item For each hidden neuron, sequentially replace its activation with the pre-noise activation value and measure the resulting change in $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$.
\item Quantify the influence of the multilayer perceptron (MLP) and attention modules within each block on $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$.
\end{enumerate}

The influence of a hidden neuron on the output probability $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$ is defined as the Indirect Effect (IE) \cite{NEURIPS2022_6f1d43d5}, which  measures the magnitude of change in $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$ between a model in which all activations are corrupted with noise and a model in which only a specific hidden neuron is restored to its original, noise-free state for the given input sequence (\textit{s}, \textit{r}).
In other words, the IE quantifies the causal contribution of that neuron.
Furthermore, by averaging the IE across multiple relational triples (\textit{s}, \textit{r}, \textit{o}), we obtain the average indirect effect (AIE) for each hidden neuron.
The AIE serves as a metric indicating the extent to which each hidden neuron contributes to factual prediction and is useful for evaluating the localization of knowledge within LLMs.

Meng et al. (2022) demonstrated that hidden neurons with high IE values are concentrated near the final token of \textit{s} and near the output token as a result of this procedure.
They also found that the MLP module contributes to the hidden neurons near the last token of \textit{s}, whereas the attention module contributes near the output.

The MLP module is represented as
\begin{equation}
\text{MLP}(\mathbf{x}) = \text{ReLU}(\mathbf{x} \cdot \mathbf{W}_1 + \mathbf{b}_1) \cdot \mathbf{W}_2 + \mathbf{b}_2. \label{eq:mlp}
\end{equation}
where $\mathbf{x}$ is the input vector to the MLP module, $\mathbf{W}_1$ and $\mathbf{W}_2$ are weight matrices for the first and second layers, respectively, and $\mathbf{b}_1$ and $\mathbf{b}_2$ are the corresponding bias vectors.
$\text{ReLU}(\cdot)$ denotes the rectified linear unit activation function, which applies element-wise nonlinearity.
According to Geva et al. (2021), each layer of the MLP in a transformer model functions as a key-value memory: the input to the MLP acts as a query, the first layer represents the key, and the second layer represents the value.
Assuming that the key value structure plays the role of recalling knowledge, Meng et al. (2022) proposed that the MLP serves as the storing mechanism of knowledge.

On the basis of these findings and given the observation that the hidden neurons near the last subject token are activated by the MLP module, we infer that the knowledge neurons are located in the MLP module corresponding to the last subject token.
This observation was consistent across different models.
Therefore, in the locating process, the layer containing the MLP module with the highest IE value can be identified.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Editing}

Consider the case of editing from (\textit{s}, \textit{r}, \textit{o}) to (\textit{s}, \textit{r}, \textit{o*}) as the editing setting.
In this procedure, the weights of the second layer, which is assumed to represent the value within the identified MLP module, are modified.
First, (\textit{s}, \textit{r}) is input during the locating step.
The value mapped from the key corresponding to (\textit{s}, \textit{r}) is then replaced with the value corresponding to \textit{o*}.
A notable aspect of the editing process is that it solves an optimization problem designed to maximize $p(\mathbf{o^*} | \mathbf{s}, \mathbf{r})$ while constraining changes to ensure that other types of knowledge are not affected.
In other words, the method iteratively updates the parameters under a constraint condition that targets the desired factual change while preserving unrelated knowledge.
This constraint allows the procedure to update only the target knowledge while retaining the rest of the model's stored information.
Furthermore, the number of iterative optimization steps influences both $p(\mathbf{o^*} | \mathbf{s}, \mathbf{r})$ and impact on other knowledge.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Proposed Method}

In ROME, knowledge neurons are identified during the locating step using the \((s,r)\rightarrow o\) format, in which only the subject token is perturbed with slight noise, and the remaining tokens are treated as the relation.
However, in languages with SOV word order, such as Japanese, the relation \textit{r} tends to appear at the end of the sentence. 
Therefore, when applying ROME in a manner natural to Japanese, the input may lack part of the relation or result in a syntactically incomplete sentence, causing difficulty in constructing the \((s,r)\rightarrow o\) format while preserving the intended meaning.
One possible solution is to reorder the sequence according to the word order, adopting the \((s,o)\rightarrow r\) format.
However, in this case, the remaining tokens would be interpreted as contextual information representing the relation but instead be treated as an object—that is, as a proper entity.
As a result, the relational meaning may be lost, and the processing under ROME's assumptions may not function as intended.


To address this issue, this paper proposes a question-answering (QA) format that transforms the input into a question incorporating the content of \((s,r)\), with \textit{o} generated as the answer.
As illustrated in Figure \ref{fig:flow_image}, the relationship is embedded within an interrogative phrase, enabling a natural word order while preserving the assumptions of the locating step.
This template can be applied across different languages, including those with non-canonical word orders such as Romanian and Basque (OVS type), as well as languages with complex verb structures such as German, which has separable verbs.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 2
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f02.eps}
\end{center}
\hangcaption{Comparison of the transformation processes in ROME (top) and the proposed method (bottom). This figure illustrates how each method converts knowledge in the (\textit{s}, \textit{r}, \textit{o}) format into an input sentence for editing. In ROME, sentences with the object (\textit{o}) omitted are used as input for the LLM to locate and edit knowledge neurons. As a result, when ROME is applied to languages such as Japanese, where the object does not appear at the end of the sentence, the input word sequences may not form grammatically valid sentences, hindering accurate locating and editing. In contrast, the proposed method resolves this issue by enabling edits without imposing word-order constraints.
}
\label{fig:flow_image}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


Note that the proposed method cannot fully complete the locating operation simply by changing the input sentence format.
In ROME, for example, since the input sentences end with phrases like ``～ of'' or ``～ in,'' the word that the LLM outputs following the input is likely to be the expected \textit{o}.
Therefore, locating can be performed by directly observing the generation probability of the output word.
In the proposed method, since the input sentence ends with ``～?,'' the answer is output as a sentence, and the word output following the input is less likely to be the expected \textit{o}.
To address this problem in the proposed method, instead of observing the generation probability of the word immediately following the input, we decided to observe the generation probability of the expected \textit{o} within the full probability distribution over the vocabulary computed when generating the continuation of the input.
This enables the proposed method to identify the activation related to a specific (\textit{s}, \textit{r}, \textit{o}).


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Experimental Setup}

The implementation in this study was based on the official GitHub repository of ROME.  
In our environment, in addition to the libraries listed in ROME's \texttt{requirements.txt}, we also used commonly employed Python libraries for LLM control and evaluation, as well as standard built-in Python libraries.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Datasets}\label{subsec:datasets}

In this study, we designed a dataset based on the known-facts used in prior research to enable comparison with ROME. 
First, we extracted the ``subject,'' ``prompt,'' and ``attribute'' fields and constructed (\textit{s}, \textit{r}, \textit{o}) triples.
A specific example is presented in Table \ref{tab:known_facts}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 1
\begin{table}[t]
\input{02table01.tex}%
\caption{Example of known-facts dataset.}
\label{tab:known_facts}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

ROME aims to precisely evaluate the factual knowledge-editing capabilities of LLMs, and constructs the known-facts dataset by extracting 1,209 instances from the COUNTERFACT dataset \cite{NEURIPS2022_6f1d43d5} for which the GPT-2 XL model was able to correctly generate \textit{o}.
COUNTERFACT is a dataset designed to evaluate the factual knowledge-editing performance of models, with a structure that allows for simultaneous assessment of post-edit generalization and specificity.
Furthermore, 1,000 instances were randomly sampled from the known-facts dataset to create an evaluation dataset for the GPT-2 XL model.
For models other than GPT-2 XL, only the instances from these 1,000 in which the respective model could correctly generate \textit{o} were used, resulting in model-specific datasets.
For example, for EleutherAI/gpt-j-6B\footnote{\url{https://huggingface.co/EleutherAI/gpt-j-6b}}, the number of applicable instances was 501.


In this study, while aiming to conduct comparisons under the same conditions as in ROME, we utilized all 1,209 instances from the original dataset because the details of the 1,000 randomly sampled data points used in ROME are not publicly available.
We evaluated whether each model could correctly generate \textit{o} given (\textit{s}, \textit{r}), and selected the instances accordingly.
Consequently, 784 instances were selected for the English LLM EleutherAI/gpt-j-6B, 840 for meta-llama/Llama-3.2-3B\footnote{\url{https://huggingface.co/meta-llama/Llama-3.2-3B}}, 230 for the Japanese LLM rinna/japanese-gpt-neox-3.6b\footnote{\url{https://huggingface.co/rinna/japanese-gpt-neox-3.6b}}, and 555 for SakanaAI/TinySwallow-1.5B\footnote{\url{https://huggingface.co/SakanaAI/TinySwallow-1.5B}}.


Because the known-facts dataset does not contain \textit{o*}, which corresponds to the edited object, we assigned \textit{o*} by randomly selecting other entities appropriate to the category for each (\textit{s}, \textit{r}) pair.
Specifically, all \textit{o} values were extracted from the (\textit{s}, \textit{r}, \textit{o}) triples and categorized into semantically coherent groups to avoid unnatural substitutions.
We defined 23 semantic categories, such as ``religion,'' ``city,'' and ``language.''
For eight of these categories, with fewer than three candidate entities, we manually added an average of 1.625 entities considered plausible, ensuring that each category contained at least three candidates.
Subsequently, for each (\textit{s}, \textit{r}) pair, a different entity from the same category was randomly selected to serve as \textit{o*}.
This method guarantees that each (\textit{s}, \textit{r}) pair has at least two choices.
Appendix \ref{app:o_star} provides a full list of categories and entities.


We refer to the 1,209-instance dataset, augmented with \textit{o*} in the known-facts dataset, as Dataset\_1.
Next, the prompts in Dataset\_1 were converted into English interrogative sentences using the OpenAI API model gpt-4o-mini-2024-07-18 to create Dataset\_2.
Using the same model, we translated Dataset\_2 into Japanese to create Dataset\_3.
The prompts used in both the English and Japanese translations are provided in Appendix \ref{app:conversion_prompt}.
Because translation was required to convert Dataset\_2 into Dataset\_3, we used GPT for this process.
For consistency in the processing pipeline, we also used GPT to convert Dataset\_1 into Dataset\_2 instead of using a template-based approach.


During the translation into Dataset\_3, the proper nouns were retained in their original English form to prevent token splitting.
In addition, all 1,209 converted and translated instances were manually inspected to ensure that no semantic distortions occurred.
Furthermore, because of the requirements of both ROME and our proposed method, the subject must be included in the sentence targeted for editing.
We confirmed that the subject was preserved in all 1,209 converted and translated sentences.
For example, in the triple (Beats Music, is owned by, Apple), the generated question was ``Which company owns Beats Music?,'' thereby confirming the presence of ``Beats Music'' in the sentence.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Experimental Overview}

First, we conducted locating and editing experiments based on ROME using Dataset\_1 and the proposed method using Dataset\_2 on English LLMs (EleutherAI/\-gpt-j-6B and meta-llama/\-Llama-3.2-3B).
In addition, we conducted locating and editing experiments on Japanese LLMs (rinna/\-japanese-gpt-neox-3.6b and SakanaAI/\-TinySwallow-1.5B) using Dataset\_3.


In the locating experiments, we selected data from the known-facts dataset for each model and calculated the Average Indirect Effect (AIE) at each layer and token position.
For the English LLMs, we compared the results of ROME with the proposed method, whereas for the Japanese LLMs, we evaluated only the proposed method.


In the editing experiments, we used the same data as in the locating experiments and applied the proposed method to perform individual edits across 20 steps.
The number of editing steps is a key hyperparameter because its selection affects both the generalization performance and editing efficiency of the knowledge-editing method.
Therefore, we quantitatively analyzed how varying the step count influences editing performance.
After editing, we prompted the model with ``{subject} is'' (in English) or ``subjectは'' (in Japanese) to generate up to 100 tokens and evaluated whether \textit{o*} appeared in the output.


Furthermore, for EleutherAI/gpt-j-6B, we compared ROME and the proposed method in terms of changes in $p(\mathbf{o^*} | \mathbf{s}, \mathbf{r})$ when continuously editing 100 randomly sampled instances.
The analysis was conducted in two stages.
First, we compared the distribution of $p(\mathbf{o^*})$ for the most recent edit by computing the generation probability $p(\mathbf{o^*})$ immediately after each editing operation and plotting its histogram. 
Second, we compared the fluctuation patterns of $p(\mathbf{o^*})$ during continuous editing of 100 knowledge entries.
Specifically, we visualized how well the previously edited knowledge was retained as a function of the offset of editing steps (i.e., how many steps earlier the knowledge was edited).
To evaluate the influence of the edit order, we conducted experiments using three different shuffled edit sequences and present the result corresponding to one of them in the main text.
The remaining two sequences are provided in Appendix \ref{app:two_shuffled_edit_orders}.
All comparisons between ROME and the proposed method were conducted using the same editing order and dataset.


In addition, to qualitatively evaluate the quality of the generated output, we analyzed the presence of \textit{o*} in the output after editing using EleutherAI/gpt-j-6B for English and rinna/japanese-gpt-neox-3.6b for Japanese.
Specifically, we collected output examples in which the edits were successfully reflected (success cases) and those in which the expected \textit{o*} did not appear (failure cases), and analyzed the representative characteristics of each.
Furthermore, for rinna/japanese-gpt-neox-3.6b, we counted all failure cases and analyzed their patterns.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 3 and 4
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f03.eps}
\end{center}
\hangcaption{AIE of each hidden neuron's activation on $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$ across layers in the English LLM EleutherAI/gpt-j-6B.
The input example represents a single input instance, and the plotted values indicate the mean and 95\% confidence interval over 784 cases.}
\label{fig:locating_eng_gptj}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f04.eps}
\end{center}
\hangcaption{AIE of each hidden neuron's activation on $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$ across layers in the English meta-llama/Llama-3.2-3B.
The input example represents a single input instance, and the plotted values indicate the mean and 95\% confidence interval over 840 cases.}
\label{fig:locating_eng_llama}
\vspace{-0.5\Cvs}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Results}\label{sec:results}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Locating}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{English LLM}

The AIE and its 95\% confidence interval, attributable to the activation of each neuron at each token position in the English LLMs,
are shown in Figure \ref{fig:locating_eng_gptj} for EleutherAI/gpt-j-6B and in Figure \ref{fig:locating_eng_llama} for meta-llama/Llama-3.2-3B.
These figures show the AIE for the hidden neuron, MLP module, and attention module in both ROME and the proposed method.
From top to bottom, the plots correspond to the AIE of each neuron's activation at the ``First subject token,'' ``Middle subject tokens,'' ``Last subject token,'' ``First subsequent token,'' ``Further tokens,'' and ``Last token'' positions.
Explaining the ``input example'' in the figure, and using the left panel as an illustration, when observing the probability of generating ``Angels'' given the input ``Shohei Ohtani is a member of the'' (using the EleutherAI/gpt-j-6b tokenizer), ``sh'' is the ``First subject token,'' ``ohei Oht'' are the ``Middle subject tokens,'' ``ani'' is the ``Last subject token,'' ``is'' is the ``First subsequent token,'' ``a member of'' are the ``Further tokens,'' and ``the'' is the ``Last token.''

Overall, 
\pagebreak
the AIE trends were mostly consistent between ROME and the proposed method. Among these, the ``Last token'' position and the ``Last subject token'' position were considered the most important.
At the ``Last token'' position, we observed that the AIE of the hidden neuron and the attention module was high in the later layers.
Furthermore, at the ``Last subject token'' position, which is crucial for identifying knowledge neurons, the AIE of the hidden neuron was high in the early layers for both methods, and the peak positions were almost identical.
Since the layer where the AIE of the hidden neuron peaks at the ``Last subject token'' position is considered to contain the knowledge neuron, this result confirms that the knowledge neurons identified by both methods are consistent.
On the other hand, for the AIE of the hidden neuron, the proposed method, unlike ROME, showed a high AIE in the later layers at the ``First subsequent token'' position, similar to the ``Last token'' position.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 5
\begin{figure}[b]
\begin{center}
\includegraphics{32-4ia2f05.eps}
\end{center}
\hangcaption{AIE of each hidden neuron's activation on $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$ across layers in the Japanese LLM rinna/japanese-gpt-neox-3.6b (proposed method). The input example represents a single input instance, and the plotted values indicate the mean and 95\% confidence interval over 230 cases.}
\label{fig:locating_jp_rinna}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Japanese LLM}

The average indirect effect (AIE) and its 95\% confidence interval, attributable to the activation of each neuron at each token position in the English LLMs, are shown in Figure \ref{fig:locating_jp_rinna} for rinna/japanese-gpt-neox-3.6b and in Figure \ref{fig:locating_jp_swallow} for SakanaAI/TinySwallow-1.5B.
Focusing on the positions of the last subject token and the last token, we confirmed that the trend of increase and decrease is similar to the previous results of ROME.
On the other hand, for rinna/japanese-gpt-neox-3.6b, a generally flat trend was observed. 
Additionally, for SakanaAI/TinySwallow-1.5B, a large AIE was observed in the attention module in the early layers, specifically at last subject token position.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 6
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f06.eps}
\end{center}
\hangcaption{AIE of each  hidden neuron's activation on $p(\mathbf{o} | \mathbf{s}, \mathbf{r})$ across layers in the Japanese LLM SakanaAI/TinySwallow-1.5B (proposed method). The input example represents a single input instance, and the plotted values indicate the mean and 95\% confidence interval over 555 cases.}
\label{fig:locating_jp_swallow}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Editing} \label{Editing}

We analyzed the relationship between the number of editing steps and the average \textit{o*} detection rate for each model. 
Four LLMs were targeted: two English LLMs—EleutherAI/gpt-j-6B and meta-llama/Llama-3.2-3B—and two Japanese LLMs—rinna/japanese-gpt-neox-3.6b and SakanaAI/TinySwallow-1.5B. 
After each editing operation, we prompted the English models with ``{subject} is'' and the Japanese models with ``subjectは,'' generating 100 tokens and checking whether the generated text contained the target knowledge \textit{o*}. 
The relationship between the number of editing steps and the average number of \textit{o*} detections is shown in Figure \ref{fig:Comparison_of_the_Average_Number_of_Detections_per_Editing_Step_across_Models} (vertical axis: average number of detections; horizontal axis: number of editing steps).


Figure \ref{fig:Comparison_of_the_Average_Number_of_Detections_per_Editing_Step_across_Models} depicts that for all models, performance improves with successive editing steps and stabilizes beyond a certain number of steps. 
For example, SakanaAI/TinySwallow-1.5B stabilizes after 11 steps, and EleutherAI/gpt-j-6B stabilizes after 7 steps. 
A comparison between English models (EleutherAI/gpt-j-6B, meta-llama/Llama-3.2-3B) and Japanese models (rinna/japanese-gpt-neox-3.6b, SakanaAI/TinySwallow-1.5B) reveals no significant differences in performance trends or stabilization behavior. 
Notably, EleutherAI/gpt-j-6B and SakanaAI/TinySwallow-1.5B, which achieve the most stable results, exhibit high editing performance despite being designed for different languages. 
Moreover, the results indicate that model size does not directly correlate with performance. 
For instance, SakanaAI/TinySwallow-1.5B, with 1.5B parameters, outperforms EleutherAI/gpt-j-6B, which has 6B parameters, suggesting that the applicability of the editing method and model design have a significant impact on performance.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 7
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f07.eps}
\end{center}
\hangcaption{Successful detection rate of edited knowledge \textit{o*} per editing step. The number of evaluated samples for each model was as follows: 784 for EleutherAI/gpt-j-6B, 840 for meta-llama/Llama-3.2-3B, 230 for rinna/japanese-gpt-neox-3.6b, and 555 for SakanaAI/TinySwallow-1.5B.
}
\label{fig:Comparison_of_the_Average_Number_of_Detections_per_Editing_Step_across_Models}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{English LLM}

We conducted continuous knowledge editing with a fixed number of 20 editing steps for 100 instances, and analyzed the changes in $p(\mathbf{o^*} \mid \mathbf{s}, \mathbf{r})$. 
The histogram of these values is shown in Figure \ref{fig:hist}. 
The proportion of instances in which $p(\mathbf{o^*} \mid \mathbf{s}, \mathbf{r})$ exceeded 0.95 was 56\% (56 out of 100) for ROME and 100\% (100 out of 100) for our proposed method. 
The average value was 0.770 for ROME and 0.998 for our method, whereas the variance was 0.105 for ROME and $3.23 \times 10^{-6}$ for our method.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 8
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f08.eps}
\end{center}
\hangcaption{Histogram of the updated $p(\mathbf{o^*} | \mathbf{s}, \mathbf{r})$ after sequentially editing 100 instances with 20 editing steps in the English LLM EleutherAI/gpt-j-6B.}
\label{fig:hist}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 9
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f09.eps}
\end{center}
\hangcaption{Change in $p(\mathbf{o^*} | \mathbf{s}, \mathbf{r})$ with respect to the number of edits elapsed during 100 consecutive editing operations on the English LLM EleutherAI/gpt-j-6B.}
\label{fig:hist_offset}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


Figure \ref{fig:hist_offset} displays the extent to which previously edited knowledge was retained when continuous edits were applied to 100 instances of (\textit{s}, \textit{r}, \textit{o*}), based on the offset of the edit step (i.e., how many edits ago the knowledge was inserted). 
Here, ``offset = k'' denotes the state in which a particular knowledge item, \textit{o*}, inserted at the $i$-th editing step is evaluated after $k$ subsequent edits have been applied. 
For example, offset = 0 evaluates the immediate reflection of the edit (reflection performance), whereas offset = 99 evaluates whether the knowledge is still retained after 99 subsequent edits (retention performance). 
The left panel shows the results for ROME, and the right panel shows the results for the proposed method; both use the same order and dataset. 
The horizontal axis indicates the offset from the current edit, and the vertical axis shows the mean and standard deviation of the output probability $p(\mathbf{o^*})$ for the target knowledge.


In the proposed method (right panel), $p(\mathbf{o^*})$ at offset = 0 is very close to 1, indicating highly accurate reflection. 
However, at offset = 1, a sharp decline is observed, suggesting that strong overwriting of prior knowledge occurs immediately after a new edit. 
In contrast, ROME (left panel) shows slightly lower probabilities at the latest edit but demonstrates a more gradual retention of past knowledge, even as edits accumulate.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 10
\begin{figure}[b]
\begin{center}
\includegraphics{32-4ia2f10.eps}
\end{center}
\hangcaption{Successful editing example on EleutherAI/\-gpt-j-6B. The edited word ``Sanskrit'' started to appear around step 15, with the generated text maintaining coherent context.}
\label{fig:successful_english_examples}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% fig. 11
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f11.eps}
\end{center}
\hangcaption{Failed editing example on EleutherAI/gpt-j-6B. The edited word ``Welsh'' appeared up to around step 15, but the text began to break down and excessively repeat ``Welsh'' closer to step 20.}
\label{fig:failed_english_examples}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


We also qualitatively analyzed representative examples of successful and failed edits when using EleutherAI/gpt-j-6B by presenting cases where the edit was accurately reflected (success) and those where it was not (failure), as shown in Figure \ref{fig:successful_english_examples} and Figure \ref{fig:failed_english_examples}.
In the successful examples shown in Figure \ref{fig:successful_english_examples}, the post-edit word ``Sanskrit'' begins to appear around step 15 and continues to be generated consistently in a coherent manner.
During the initial steps (0–10), the target word does not appear, but the generated sentences remained coherent.
From step 15 onward, natural sentences containing ``Sanskrit'' are generated continuously, and similar behavior is observed at step 20.
Overall, in 20–30\% of the cases, the post-edit word appears as early as in step 3, whereas in the remaining 70–80\%, it first appears around step 7.
Cases in which the post-editing word does not appear even after 10 steps are very few, and in most instances, once the word appears, it continues to be generated stably in subsequent steps.
By contrast, in the failed examples shown in Figure \ref{fig:failed_english_examples}, the post-edit word ``Welsh'' begins to appear from step 5 without breaking sentence coherence, and editing behavior up to step 15 can be interpreted as successful. 
However, from step 15 onward, ``Welsh'' is excessively repeated, and by step 20, the sentence as a whole collapses. 
In terms of overall trends in failure cases, approximately 80–90\% show no change (i.e., the post-edit word is never output). 
The remaining 10–20\% of failures involve repetitive sentences or excessive output of the post-edit word, but clearly identifiable failure patterns are limited. 
Two main trends are observed in the timing of failures: failures appear around step 7 and persist thereafter, and a ``late-onset'' pattern where sentence degradation begins after step 15.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Japanese LLM}
\label{subsec:Results_EditingJapanese}

Following the analysis conducted for English LLMs, we examined representative output cases of rinna/japanese-gpt-neox-3.6b.
To identify common failure tendencies, we closely inspected the outputs and extracted characteristic patterns of errors.
The relationship between the number of editing steps and the average \textit{o*} detection rate was analyzed in Section \ref{Editing}, where we observed that the performance of rinna/japanese-gpt-neox-3.6b tended to stabilize at approximately seven editing steps (Figure \ref{fig:Comparison_of_the_Average_Number_of_Detections_per_Editing_Step_across_Models}).
Based on this finding, in this section, we evaluated the outputs of rinna/japanese-gpt-neox-3.6b under a fixed setting of seven editing steps and categorized and organized the failed outputs.
Specifically, cases in which the edits were not reflected as expected were classified into multiple categories, and the number and proportion of cases in each category were summarized in a tabular format.
Although the inputs and outputs for the Japanese LLM were in Japanese, the figures display English translations of the results to aid reader comprehension.
Appendix \ref{app:japanese_output_before_translation} presents the original Japanese output prior to translation.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 12
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f12.eps}
\end{center}
\hangcaption{Successful editing example on rinna/japanese-gpt-neox-3.6b. German-related words started appearing around step 7, and the edited word ``German'' appeared from step 10. Coherent generation was maintained up to step 20.}
\label{fig:successful_examples}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


As a successful example, the output shown in Figure \ref{fig:successful_examples} demonstrates how the post-edit word ``German'' gradually begins to appear.
At steps 0 and 5, the original knowledge concerning the ``United Kingdom'' remains predominant, and the edit has not yet been reflected.
However, at step 7, references to German-speaking countries such as ``Germany'' and ``Austria'' begin to emerge, indicating signs of knowledge transition.
At step 10, the word ``German'' appears for the first time, and by steps 15 and 20, post-edit terms such as ``German alphabet'' and ``official languages'' are consistently used.
This example in Figure \ref{fig:successful_examples} illustrates a typical successful pattern in which the edit gradually permeates from the mid-stage and becomes stable in the latter steps.
For failure cases, as shown in Figure \ref{fig:failed_examples}, the most prominent failure pattern observed is the excessive repetition of the post-edit word.
Specifically, although step 10 still generates descriptions related to ``Australia'' within a minimal contextual boundary, from step 15 onward, the word ``Australia'' begins to be excessively repeated.
By step 20, only punctuation marks are repeatedly output, and the coherence of the sentence is completely lost.
Such an output collapse often follows a trend in which once a failure occurs at a certain step, subsequent steps continue to fail or the output stops changing entirely.
In only 20–30\% of cases, the expected output is recovered after an initial failure; in the remaining majority, the broken state persists.
In terms of the timing of the first failure, 70–80\% of the samples show initial failure between steps 5 and 10, whereas the remaining 20–30\% exhibit failures after step 10.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 13
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f13.eps}
\end{center}
\hangcaption{Failed editing example on rinna/japanese-gpt-neox-3.6b. The edited word ``Australia'' appeared coherently up to step 10, but from around step 15, it was excessively repeated, and by step 20, repeated punctuation marks also appeared.}
\label{fig:failed_examples}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 14
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f14.eps}
\end{center}
\hangcaption{Failed outputs at step 7 on rinna/japanese-gpt-neox-3.6b, categorized into five types: sentence repetition, punctuation repetition, unrelated word repetition, edited word repetition, and no change.}
\label{fig:kind_of_failure_7step_examples}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Upon inspecting the outputs of rinna/japanese-gpt-neox-3.6b following seven-step edits, we find that only approximately 3\% of cases (seven instances) are successful and the remaining 97\% (223 instances) contain some form of failure (Table \ref{tab:editing_classification}).
As illustrated in Figure \ref{fig:kind_of_failure_7step_examples}, the failed outputs are classified into the following five categories:
\begin{enumerate}[label=(\roman*)]
    \item Repetition of a sentence (e.g., repeated occurrences of ``means old-fashioned, antiquated, and so on.'')
    \item Repetition of punctuation (e.g., excessive repetition of periods or subject-predicate structures)
    \item Repetition of words unrelated to the post-edit word (e.g., abnormal repetition of ``Portugal'')
    \item Repetition of the post-edit word itself (e.g., excessive use of ``Buddhist'')
    \item Outputs with no semantic change caused by the edit (e.g., continuing to generate text based on ``Apple'' as before the edit)
\end{enumerate}
Notably, the  ``no change'' category accounted for the overwhelming majority (194 instances; 87.0\%), followed by ``repetition of a sentence'' (16 instances; 7.17\%), and  ``repetition of unrelated words'' (10 instances; 3.41\%).

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% table 2
\begin{table}[t]
\input{02table02.tex}%
\hangcaption{Classification of editing outcomes at the seven-step point in the Japanese LLM rinna/japanese-gpt-neox-3.6b (proposed method).}
\label{tab:editing_classification}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Discussion}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Locating}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{English LLM}

The observation that the peak AIE of hidden neurons at the ``last subject token'' position coincided for both methods suggests that the proposed method is effective in identifying knowledge neurons, similar to ROME. 
In contrast, the proposed method also exhibited high AIE in the later layers at the ``first subsequent token'' position. 
This phenomenon is likely because of cases where \textit{s} appears near the end of the input sentence, causing the first subsequent token to effectively overlap with the last token.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Japanese LLM}

First, the generally flat trend observed in rinna/japanese-gpt-neox-3.6b suggests that the locality of knowledge within the model may be weak, causing difficulty in forming knowledge representations that rely on specific layers or token positions. 
This may be attributed to the data distribution and training objectives during pretraining, or to factors such as model size and architectural design.


Next, the observation of a large AIE in the early layers of the attention module at the last subject token position in SakanaAI/TinySwallow-1.5B implies that knowledge retrieval may be dynamically regulated through not only the MLP layers but also the attention layers. 
Such differences are likely to reflect variations in knowledge encoding arising from differences in model scale and training methods.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Editing}

The trend shown in Figure \ref{fig:Comparison_of_the_Average_Number_of_Detections_per_Editing_Step_across_Models} depicts that the editing method proposed in this study is robust against differences in language and model architecture.
In particular, the observation of consistent performance trends across languages with significantly different grammatical structures, such as Japanese and English, strongly supports the conclusion that the proposed method is not dependent on word order or linguistic expressions.
Furthermore, the fact that a medium-sized Japanese model, such as SakanaAI/TinySwallow-1.5B, achieved comparable or even superior performance to a large-scale English model (EleutherAI/gpt-j-6B) reveals performance differences that cannot be explained solely by the number of parameters.
Additionally, the common trend observed across all models, where performance improves with an increasing number of editing steps and stabilizes beyond a certain threshold—indicates that the proposed method possesses robustness, because no indication of performance degradation existed during the editing process, even when the number of steps was increased.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{English LLM}

The results shown in Figure \ref{fig:hist_offset} reveal that the proposed method (right panel) achieves a nearly perfect value of $p(\mathbf{o^*})$ at the most recent edit (offset = 0), indicating that the knowledge is reflected with high accuracy.
However, as soon as the next edit (offset = 1) is performed, $p(\mathbf{o^*})$ for the previously edited knowledge drops sharply, suggesting that a strong overwrite has occurred.
In other words, although the proposed method excels in high-precision single-shot editing, it faces challenges in consistently retaining previously edited knowledge during sequential editing.
In contrast, with ROME (left panel), although the average $p(\mathbf{o^*})$ at offset = 0 remains 0.8, the value decreases gradually as the offset increases, indicating that previously edited knowledge is relatively well-preserved even after multiple sequential edits.
These results suggest that ROME is inferior to the proposed method in terms of immediate edit accuracy but exhibits superior stability in retaining knowledge under continuous editing.


Next, based on the results of the qualitative analysis of successful and failed examples, we examine the behavior of knowledge reflection during editing in EleutherAI/gpt-j-6B.
We discuss the characteristics of output sentences in successful cases and the tendencies of erroneous generation in failed cases to explore the effectiveness and limitations of the proposed method, as well as potential directions for future improvements.


In the successful cases, once the edited word was output at a certain step, it tended to continue being output consistently in the subsequent steps.
This suggests a gradual incorporation of the intended knowledge into the model's internal representation through editing.
In particular, the natural appearance of ``Sanskrit'' in outputs after step 15 indicates that the model was able to reflect the new information while maintaining contextual coherence.


On the other hand, in the failed cases, although appropriate outputs were initially generated, the latter steps exhibited breakdowns in sentence structure or excessive repetition of the edited word.
This suggests that when the editing operation exceeds a certain threshold, the internal representation may lose coherence, resulting in overfitted behavior or output saturation during generation.
In particular, the abnormal repetition of ``Welsh'' implies that the edited word exerted an overly strong bias on the output, likely because of excessive parameter updates or lack of control.
Overall, careful design of the number of editing steps is essential: too few steps lead to insufficient reflection, whereas too many can cause output degradation.
As per the present results, approximately 7–10 steps appear to be a relatively appropriate range in balancing edit success rate and output naturalness.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsubsection{Japanese LLM}

We conducted a qualitative analysis of the editing results for rinna/japanese-gpt-neox-3.6b, focusing on both successful and failed cases, as well as an examination of failure trends under the condition in which the number of editing steps was fixed at seven. 
Based on the classification results of the outputs and the number and proportion of occurrences in each category, we discuss the main factors behind the failure of edits to reflect as intended and highlight the characteristics suggested through comparison with English LLMs.


A comparison between successful and failed cases suggests that the process of integrating and reflecting edited knowledge into the model involves a certain delay and stabilization phase. 
As shown in Figure \ref{fig:successful_examples}, cases where the edited word begins to appear gradually around the mid-phase exist (approximately step 7) and are consistently used in the later phase (after step 10), indicating that the edit has permeated the model appropriately and is being consistently utilized during language generation. 
This implies that a certain degree of repetition is required for the edited information to overcome transient fluctuations in output and be integrated into the context.


On the other hand, as illustrated in Figure \ref{fig:failed_examples}, failure cases include outputs where the edited word initially appears in context for a few steps but subsequently leads to excessive repetition or semantic incoherence.
Such output breakdowns suggest that once an edit fails, its influence propagates to subsequent generations, causing difficulty in recovering or stabilizing the sentence.
In fact, cases in which the model recovers to generate the correct output after a failure are rare, implying that the success of an edit largely depends on how well it is retained during the early to mid-phases.
Moreover, failures frequently occurred between steps 5 and 10, suggesting that this range may serve as a boundary where knowledge retention and interference interact.
If competition or instability arises within the internal knowledge structure at this point, it may continuously affect subsequent output.


According to the results shown in Table \ref{tab:editing_classification}, one notable finding is that the majority of cases fall under the ``no change'' category.
This suggests that the edit signal is either not sufficiently propagated to the model's internal knowledge representation or that the targeted knowledge is so strongly entrenched that updating it is difficult.
Meanwhile, in failure cases involving repetition (of sentences, words, edited words, or punctuation), we infer that excessive convergence toward the embedding space of the edited word leads to collapse during language generation.
For instance, the overuse of ``Buddhist'' may indicate that the model prioritizes conformity to the edited word over contextual coherence, thereby compromising the stability of text generation.
Furthermore, syntactic breakdowns, such as repeated punctuation, exemplify cases where token-level generation control collapses, highlighting the risk that editing signals can have adverse effects.
These findings underscore the need to design a balance between the ``application'' and ``propagation'' of edits.
They strongly indicate the necessity for editing methods that not only perform local overwriting of specific words but also support ripple effects that naturally reconstruct surrounding knowledge.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Conclusion}

In this study, we proposed a new method for identifying knowledge neurons that eliminates conventional constraints and enables flexible locating regardless of whether or not the language follows an SVO structure.

First, to verify the effectiveness of the proposed method, we conducted a comparative experiment on an English model using ROME and the proposed method.
The results showed similar AIE trends for both methods, confirming that the estimated knowledge-neuron positions matched.
In terms of editing, the proposed method performed similarly to ROME in English and was applicable even to Japanese, where adapting ROME was challenging.

Next, we conducted the experiments on the Japanese language, which is an SOV language.
Although the locating performance of the proposed method for the Japanese LLM yielded significant results, we found that careful adjustments were necessary during editing to avoid adversely affecting the model.

We intend to apply the proposed method to LLMs in other languages to validate its effectiveness more comprehensively.
Through these efforts, we will strive to further develop knowledge-editing techniques and make them adaptable to diverse languages and word orders.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Future Work}

We demonstrated the effectiveness of single knowledge editing in this study, but practical applications require the capability to continuously edit multiple pieces of knowledge while preserving previously acquired knowledge and appropriately integrating new information.
To achieve this, new evaluation datasets and metrics must be developed, and detailed analyses of how the order of edits and complexity of tasks impact model performance should be conducted.
Ishigaki and Maeda (2024) proposed a framework for evaluating multiple knowledge edits and conducted an initial investigation into how these edits affect model performance \cite{ishigaki-slud-2024}.

When constructing evaluation datasets, structures that can effectively manage both the insertion and modification of knowledge must be incorporated.
In terms of evaluation metrics, indicators tailored specifically to continuous knowledge editing, such as the accuracy of knowledge after edits, retention rate of knowledge before and after edits, and degree of interference caused by new edits on existing knowledge, must be designed.
Furthermore, the impact of edit order and task complexity on the accuracy and consistency of the model's responses should be thoroughly examined.
Mitigating the ripple effects of edits on existing knowledge is also a vital area of consideration.

Evaluation techniques for continuous knowledge editing hold significant potential for application in personalized dialogue systems and systems that handle time-sensitive information.
By enabling the continuous updating of user profile information and the integration of the latest knowledge, these techniques can contribute to the development of more practical and robust systems.

To evaluate whether our knowledge-editing method possesses a more general capability for editing linguistic knowledge, using existing question-answering datasets is considered to be effective.
In particular, by leveraging benchmarks such as CommonsenseQA \cite{talmor-etal-2019-commonsenseqa}, which assesses common sense knowledge, and TruthfulQA \cite{lin-etal-2022-truthfulqa}, which evaluates whether models imitate human misconceptions or superstitions, we can analyze the impact of knowledge editing on the consistency and truthfulness of the model's responses.
In future work, we plan to further examine the generality and practicality of the proposed method through evaluation experiments using these datasets.


Furthermore, the proposed knowledge-editing method has the potential to be applied to the modification and adjustment of more nuanced linguistic knowledge, such as expressions involving adjectives and adverbs.
Developing a framework that targets the editing of these flexible linguistic expressions represents an important research direction for future work.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Ethical Considerations}

In the training of LLMs, discriminatory expressions related to politics and religion can sometimes be included in the training data.
As a result, knowledge editing could intentionally produce these expressions, and conversely, may also output them unintentionally.
In fact, during this study, we encountered cases in which such expressions were generated unintentionally, highlighting an important ethical concern on knowledge editing. 


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section*{Limitations}

This study has the following limitations:
\begin{itemize}
\item Knowledge editing has issues such as the directionality of editing, where the editing is not reflected when the subject and object of the edited knowledge are swapped, and the ripple effect \cite{Cohen2023EvaluatingTR}, where related knowledge is not appropriately changed. However, this study did not examine these issues in detail.
\item We used a decoder-based model for our validation, but we did not investigate other commonly utilized model architectures such as T5 \cite{Raffel2019ExploringTL}. The exploration of these architectures remains a topic for future research.
\item We used Japanese as a case study to investigate the possibility of knowledge editing in SOV languages. However, other SOV languages should also be addressed in future research.
\end{itemize}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\acknowledgment
The authors thank Dr. Silvia Casola for her invaluable guidance during the time we submitted the original version of this paper to ACLSRW, and Mr. Daisuke Kawakubo for his advice at the beginning of this research.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bibliographystyle{jnlpbbl_1.8}
\bibliography{02refs}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 15
\begin{figure}[p]
\begin{center}
\includegraphics{32-4ia2f15.eps}
\end{center}
\caption{Prompt for English Question Generation.}
\label{fig:prompt_en}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 16
\begin{figure}[p]
\begin{center}
\includegraphics{32-4ia2f16.eps}
\end{center}
\caption{Prompt for Japanese Question Generation.}
\label{fig:prompt_jp}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\appendix

\section{Conversion Prompt Details}\label{app:conversion_prompt}

The prompts used to generate Dataset\_2 from Dataset\_1 and Dataset\_3 from Dataset\_2 are shown in Figure \ref{fig:prompt_en} and Figure \ref{fig:prompt_jp}, respectively.
Each prompt was designed to emphasize the preservation of the original meaning and the explicit mention of the subject, while encouraging natural interrogative reformulation and appropriate Japanese translation.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Japanese Output Before Translation}
\label{app:japanese_output_before_translation}

The results discussed in Section \ref{subsec:Results_EditingJapanese} were originally output in Japanese, and the English translation provided here was obtained using machine translation. 
The model used for machine translation was ChatGPT, and the prompt is shown in Figure \ref{fig:prompt_chatgpt}.
In this section, we show the original Japanese output.
Specifically, Figure \ref{fig:successful_examples_japanese} corresponds to the results shown in Figure \ref{fig:successful_examples}, Figure \ref{fig:failed_examples_japanese} corresponds to Figure \ref{fig:failed_examples}, and Figure \ref{fig:kind_of_failure_7step_japanese_examples} corresponds to Figure \ref{fig:kind_of_failure_7step_examples}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 17
\begin{figure}[h]
\begin{center}
\includegraphics{32-4ia2f17.eps}
\end{center}
\hangcaption{Prompt used for machine translation. The Japanese version is the original, and the translated version is shown in English after being prompted with ``Please translate into English.''}
\label{fig:prompt_chatgpt}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 18
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f18.eps}
\end{center}
\caption{Original Japanese output corresponding to the results shown in Figure \ref{fig:successful_examples}.}
\label{fig:successful_examples_japanese}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 19
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f19.eps}
\end{center}
\caption{Original Japanese output corresponding to the results shown in Figure \ref{fig:failed_examples}.}
\label{fig:failed_examples_japanese}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 20
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f20.eps}
\end{center}
\caption{Original Japanese output corresponding to the results shown in Figure \ref{fig:kind_of_failure_7step_examples}.}
\label{fig:kind_of_failure_7step_japanese_examples}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\clearpage

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Category-wise Entity Lists Used in the \textit{o*} Assignment Procedure (English \& Japanese Versions)}
\label{app:o_star}

This section presents the list of categories used in the \textit{o*} assignment procedure, along with the corresponding entity lists for each category. 
The entities were categorized based on common-sense consistency, and for categories with an insufficient number of candidates, appropriate entities have been supplemented. 
The English version of this list is shown in Figure \ref{fig:o_json}, and the Japanese version is shown in Figure \ref{fig:o_japanese_json}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 21
\begin{figure}[b]
\begin{center}
\includegraphics{32-4ia2f21.eps}
\end{center}
\hangcaption{Candidate entities used for generating post-edit objects \textit{o*} , grouped into semantically coherent categories.}
\label{fig:o_json}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 22
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f22.eps}
\end{center}
\caption{Candidate entities from Figure \ref{fig:o_json} with Japanese translations of the entity names.}
\label{fig:o_japanese_json}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\clearpage

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Additional Results from the Remaining Two Shuffled Edit Orders}
\label{app:two_shuffled_edit_orders}

Figure \ref{fig:hist_appendix} and Figure \ref{fig:hist_offset_appendix} present results for two alternative shuffling orders, 
distinct from those shown in Figure \ref{fig:hist} and Figure \ref{fig:hist_offset}. 
In both cases, the left and right plots correspond to the ROME and proposed method, respectively, with evaluation metrics and settings identical to those used in the experiments described in the main text.
These figures also consistently show that the proposed method reflects the most recent edit target with very high accuracy, but earlier edit targets are more susceptible to being overwritten.
In contrast, ROME tends to retain past knowledge more stably, confirming that the trade-off between the two methods—discussed in this study—persists regardless of edit order.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 23
\begin{figure}[h]
\begin{center}
\includegraphics{32-4ia2f23.eps}
\end{center}
\hangcaption{Histogram of the updated $p(\mathbf{o^*} | \mathbf{s}, \mathbf{r})$ after sequentially editing 100 instances with 20 editing steps in the English LLM EleutherAI/gpt-j-6B, evaluated under a different shuffled edit order.}
\label{fig:hist_appendix}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\clearpage

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% fig. 24
\begin{figure}[t]
\begin{center}
\includegraphics{32-4ia2f24.eps}
\end{center}
\hangcaption{Change in $p(\mathbf{o^*} | \mathbf{s}, \mathbf{r})$ with respect to the number of edits elapsed in the English LLM EleutherAI/gpt-j-6B, evaluated under a different shuffled edit order.}
\label{fig:hist_offset_appendix}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{biography}

\bioauthor[:]{Ryoma Ishigaki}{%
Ryoma Ishigaki is a master's student at Tokyo Denki University.
He participated in the Dialogue Robot Comp 2023 as a member of Team DSML-TDU, earning recognition as an awardee.
He received the Nikkei Innovation Lab Award at YANS, the Young Researcher's Encouragement Award at SIG-SLUD, and the University Lab Exhibition Award at SIAI. 
His main research interests are in natural language processing.
He is a member of ANLP.
}

\bioauthor[:]{Jundai Suzuki}{%
Jundai Suzuki is a master's student at Tokyo Denki University.
He participated in the Dialogue Robot Comp 2023 as the sub-leader of Team DSML-TDU, earning an award, and also received the University Lab Exhibition Award at SIAI.
He participated in the Dialogue System Live Competition 7 as leader of Team dsmlRJS.
His main research interest is natural language processing.
He is a member of ANLP.
}

\bioauthor[:]{Masaki Shuzo}{%
Masaki Shuzo received his Ph.D. degree in engineering from the University of Tokyo in 2003.
He has been with Tokyo Denki University since 2017 and is currently a professor at the Faculty of Informatics, Shonan Institute of Technology.
His research interests include human–robot interaction and android science.
He is a member of ANLP.
}

\bioauthor[:]{Eisaku Maeda}{%
Eisaku Maeda received his MS degree in zoology in 1984 and Ph.D. degree in mathematical engineering in 1993 from the University of Tokyo.
He joined NTT (Nippon Telegraph and Telephone Corp.) in 1986, where he engaged in research in diverse fields including pattern recognition, machine learning, image recognition, natural language processing, and neuroscience.
In 2017, he joined the Faculty of System Design Engineering at Tokyo Denki University as a professor and has served as dean of the faculty since 2020.
He is a member of ANLP.
}

\end{biography}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\biodate


\end{document}


