The Mirage of Model Editing: Revisiting Evaluation in the Wild
Authors: Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Qi Cao, Dawei Yin, Huawei Shen, Xueqi Cheng
Accepted at: acl25

The Mirage of Model Editing: Revisiting Evaluation in the WILD
Are We Really Making Much Progress?

Wanli Yang†‡  Fei Sun‡
Jiajun Tan†‡  Xinyu Ma*  Qi Cao†  Dawei Yin*  Huawei Shen†‡  Xueqi Cheng†‡
†State Key Laboratory of AI Safety, Institute of Computing Technology, CAS
‡University of Chinese Academy of Sciences  *Baidu Inc.
yangwanli24z@ict.ac.cn  sunfei@ict.ac.cn

Abstract
Despite near-perfect results reported in the literature, the effectiveness of model editing in real-world applications remains unclear. To bridge this gap, we introduce QAEdit, a new benchmark aligned with widely used question answering (QA) datasets, and WILD, a task-agnostic evaluation framework designed to better reflect real-world usage of model editing. Our single editing experiments show that current editing methods perform substantially worse than previously reported (38.5% vs. 96.8%). We demonstrate that it stems from issues in the synthetic evaluation practices of prior work. Among them, the most severe is the use of teacher forcing during testing, which leaks both content and length of the ground truth, leading to overestimated performance. Furthermore, we simulate practical deployment by sequential editing, revealing that current approaches fail drastically with only 1000 edits. This work calls for a shift in model editing research toward rigorous evaluation and the development of robust, scalable methods that can reliably update knowledge in LLMs for real-world use1.

1 Introduction

“If you can’t measure it, you can’t improve it.”
— Lord Kelvin

Model editing (Yao et al., 2023; Wang et al., 2024d) has attracted widespread attention for its promising vision: enabling efficient and precise updates to specific knowledge within pretrained Large Language Models (LLMs) without retraining from scratch. Recent advances report near-perfect results on corresponding benchmarks (Meng et al., 2022; Wang et al., 2024b), suggesting substantial progress toward this goal. However, these results often come from synthetic, oversimplified evaluation settings (e.g., identical prompts for editing and testing; more in §4) that may fail to capture real-world complexities. This disparity raises a critical question: Can these promising results in the literature translate to practical applications?

To address this question, we propose to study model editing in QA tasks, which provide clear evaluation criteria and broad applicability. This adaptation involves two key components: a real-world dataset and realistic evaluation. For dataset, we create QAEdit, a tailored dataset derived from three widely-used QA datasets, enabling editing methods to update LLMs with answers grounded in real-world tasks. For evaluation, we propose WILD (Without Intervention, Live Decoding), a task-agnostic evaluation framework that follows standard QA evaluation protocols (Gao et al., 2024), assessing editing methods via the performance of edited LLMs on their previously failed questions.

Our initial study reveals that current advanced editing methods achieve only a 38.5% average success rate on QAEdit, significantly lower than the results reported in previous studies. This raises a question: Does the performance decline stem from QAEdit’s real-world complexity, or from the shift of synthetic to WILD evaluation?

To enable rigorous analysis, starting with single editing experiments, we evaluate six representative methods across three leading LLMs on QAEdit and two established editing benchmarks, using both evaluation frameworks. As illustrated in Figure 1, switching from synthetic to WILD evaluation consistently leads to a significant performance decline

Figure 1: Comparison of synthetic and WILD evaluation for ROME and WISE on Llama-2-7b-chat.

![Comparison of synthetic and WILD evaluation for ROME and WISE on Llama-2-7b-chat.](page_1012_320_377_246.png)

1 Code and data are released at https://github.com/WanliYoung/Revisit-Editing-Evaluation.
across editing methods and datasets. This dramatic performance gap raises two critical questions: What differences between these frameworks drive such disparity, and which one most accurately reflects editing effectiveness?

To answer them, we carefully examine the setups for both synthetic and WILD evaluations. From this, we abstract four key modules (input, generation strategy, output truncation, and metric) and analyze their variations through controlled experiments. The results expose four critical limitations in current synthetic evaluation in model editing:

① input module: using identical prompts for editing and testing overlooks the variability and unpredictability in real-world queries;

② generation strategy: teacher forcing, which feeds the ground truth as input during decoding, artificially beautifies results by disregarding potential errors in the model’s own outputs;

③ output truncation: using target answer length to truncate outputs conceals errors (e.g., repetition, irrelevant, or incorrect information) that would occur with natural stopping criteria;

④ metric: match ratio may inflate performance by rewarding partial matches of incorrect answers.

Among these issues, teacher forcing and target length truncation cause the most significant overestimation, as they rely on ground truth that is unavailable in real-world scenarios. This highlights that synthetic evaluation, reliant on such idealized or even unrealistic conditions, fails to accurately measure true editing effectiveness.

After uncovering evaluation issues via single editing analysis, we return to our initial question: how do editing methods perform under realistic conditions? In practice, editing requests arrive continuously, making sequential editing a more genuine test of real-world applicability. Under WILD evaluation, our sequential editing experiments show that current methods catastrophically fail to scale, with average success rates dropping to ~10% for only 1000 samples.

Our work, for the first time, exposes severe issues in current evaluation of model editing research and demonstrates substantial limitations of existing editing methods under real-world conditions. We hope this work will inspire more rigorous evaluation practices and motivate the development of algorithms that can truly fulfill the promise of model editing: to reliably and scalably update knowledge in LLMs for real-world applications.

Our main contributions are as follows.
• We introduce QAEdit, a benchmark tailored for real-world QA tasks, and establish a more rigorous evaluation framework, WILD.
• We reveal that published model editing results are significantly inflated, and trace this overestimation to issues in synthetic evaluation practices, identified through modular analysis.
• We expose the severe scalability challenges of current editing methods in practical applications through sequential editing experiments.

2 Related Works

2.1 Model Editing Methodologies

Existing model editing methods can be categorized into the following four types:

Extension Based. These methods update LLMs by adding trainable parameters to encode new knowledge, e.g., additional neurons in FFN (Dong et al., 2022; Huang et al., 2023) or specialized memory modules (Hartvigsen et al., 2023; Wang et al., 2024b), while preserving pretrained weights.

Fine-tuning Based. Fine-tuning offers a straightforward approach to update LLMs’ knowledge but faces catastrophic forgetting. Recent works mitigate this by constraining parameter changes (Zhu et al., 2020) or leveraging Parameter-Efficient Fine-Tuning (PEFT) (Han et al., 2024) to limit modification scope (Yu et al., 2024; Wang et al., 2024a).

Meta Learning. Employing meta learning, KE (De Cao et al., 2021), MEND (Mitchell et al., 2022), and MALMEN (Tan et al., 2024) train hypernetworks to predict effective gradients or parameter alterations for knowledge integration.

Locate-Then-Edit. Based on the investigation of knowledge mechanisms in LLMs (Geva et al., 2021, 2022), KN (Dai et al., 2022), ROME (Meng et al., 2022), and PMET (Li et al., 2024b) utilize knowledge attribution and causal tracing to pinpoint target knowledge to specific parameters, then perform localized editing. Furthermore, MEMIT (Meng et al., 2023) and EMMET (Gupta et al., 2024c) extend this for massive editing in a batch.

2.2 Evaluation of Model Editing

Current evaluation of model editing primarily focuses on editing effectiveness and side effects on model capabilities.
Effectiveness of Editing. The effectiveness of editing is typically evaluated from four key properties using artificial benchmarks and simplified evaluation settings: ① reliability, success rate of editing; ② generalization, adaptability of edited knowledge to paraphrased prompts; ③ locality, impact on irrelevant knowledge; ④ portability, applicability of edited knowledge in factual reasoning. For detailed information, We refer readers to Yao et al. (2023). In addition to these basic metrics, domain-specific editing tasks have been introduced, e.g., privacy preservation (Wu et al., 2023), bias mitigation (Chen et al., 2024b), and harm injection (Chen et al., 2024a).

Side Effects of Editing. Recent research has also examined the potential side effects of editing on LLMs (Hoelscher-Obermaier et al., 2023; Li et al., 2024c). While locality shares similar objectives, its limited evaluation scope fails to capture the full extent of editing side effects. Recent studies (Yang et al., 2024a; Gu et al., 2024; Gupta et al., 2024b) have revealed that model editing can significantly compromise LLMs’ downstream tasks capabilities, motivating a growing research to mitigate such side effects (Ma et al., 2025; Fang et al., 2025).

Discussion. In contrast to prior efforts that either benchmark editing algorithms on synthetic datasets or analyze their side effects, this work offers the first systematic re-examination of model editing under realistic deployment conditions. While AKEW (Wu et al., 2024) shares our motivation of advancing model editing toward more realistic use cases, it pursues this goal by applying editing to a more complex task: unstructured editing. Our study instead re-evaluates the effectiveness of existing editing methods on the same basic QA tasks adopted in prior work, but under a more rigorous and realistic evaluation protocol, revealing their limited practical utility and uncovering the pitfalls of traditional editing evaluation.

3 QAEdit

Motivation. While existing work reports remarkable success of model editing techniques (Meng et al., 2022; Wang et al., 2024b), their effectiveness in real-world applications remains unclear. To rigorously examine their practical utility, we focus on the most fundamental and widely studied task of QA rather than more complex settings such as multi-hop and unstructured editing. This choice is motivated by a simple premise: if current editing methods struggle on basic QA tasks, then they are unlikely to succeed in more challenging scenarios, whereas failure in such tasks does not entail failure on the basic QA task.

Specifically, we apply editing methods to correct LLMs’ errors in QA tasks and assess the improvement by re-evaluating edited LLMs on a standard QA evaluation framework, Im-evaluation-harness (Gao et al., 2024).

Benchmark Preparation. Since existing editing benchmarks are not derived from or aligned with mainstream QA tasks, we introduce QAEdit, a tailored benchmark to rigorously assess model editing in real-world QA. Specifically, QAEdit is constructed from three widely-used QA datasets with broad real-world coverage: Natural Questions (Kwiatkowski et al., 2019), TriviaQA (Joshi et al., 2017), and SimpleQA (Wei et al., 2024). Details about these datasets are provided in Appendix A.1.

While these benchmarks provide questions and answers as edit prompts and targets respectively, they lack essential fields that mainstream editing methods require for editing and evaluation. To obtain required subjects for editing, we employ GPT-4 (gpt-4-1106-preview) to extract them directly from the questions. To align with the previous editing evaluation protocol, we evaluate: i) reliability using original edit prompts; ii) generalization through GPT-4 paraphrased prompts; and iii) locality using unrelated QA pairs from ZsRE locality set2.

As a result, QAEdit contains 19,249 samples across ten categories, ensuring diverse coverage of QA scenarios. Figure 2 shows a QAEdit entry with all fields. Dataset construction and dataset statistics are detailed in Appendix A.2.

<table>
  <tr>
    <th>Method</th>
    <th>FT-M</th>
    <th>MEND</th>
    <th>ROME</th>
    <th>MEMIT</th>
    <th>GRACE</th>
    <th>WISE</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td>Accuracy</td>
    <td>0.611</td>
    <td>0.333</td>
    <td>0.585</td>
    <td>0.552</td>
    <td>0.012</td>
    <td>0.216</td>
    <td>0.385</td>
  </tr>
</table>

Table 1: Accuracy of edited Llama-2-7b-chat on questions it failed before editing in QAEdit.

![An example from QAEdit.](page_1012_120_388_120.png)

Figure 2: An example from QAEdit.
Preliminary Study. We conduct single-edit experiments on Llama-2-7b-chat’s failed questions in QAEdit (detailed in §5). As shown in Table 1, after applying SOTA editing methods, the edited models achieve only 38.5% average accuracy under QA evaluation, far below previously reported results (Meng et al., 2023; Wang et al., 2024b). This raises a critical question: Is the performance degradation attributed to the real-world complexity of QAEdit, or to real-world QA evaluation?

4 A Tale of Two Evaluation Frameworks

To identify the cause of this performance gap and guide further investigation, we first delve into the experimental setup of both editing (synthetic) and QA task (WILD) evaluations. We abstract them into four key modules: input, generation strategy, output truncation, and metric. This modular paradigm enables systematic comparison between the two evaluation frameworks, as shown in Figure 3.

Synthetic. We formalize the evaluation pipeline commonly used in prior model editing works (Yao et al., 2023; Wang et al., 2024b) as synthetic evaluation framework, which implements the four modules in an idealized and overly simplified way (Figure 3a): ① input: using only question without additional context; ② generation strategy: employing teacher forcing to feed ground truth tokens as input during decoding3; ③ output truncation: truncating output to match the length of target answer; ④ metric: using token-level match ratio between the target and generated answer as accuracy.

<table>
  <tr>
    <th>Module</th>
    <th>Synthetic</th>
    <th>WILD</th>
  </tr>
  <tr>
    <td>Input</td>
    <td>context-free</td>
    <td>context-guided</td>
  </tr>
  <tr>
    <td>Gen. Strategy</td>
    <td>teacher forcing</td>
    <td>autoregressive decoding</td>
  </tr>
  <tr>
    <td>Output Trunc.</td>
    <td>ground truth length</td>
    <td>natural stopping criteria</td>
  </tr>
  <tr>
    <td>Metric</td>
    <td>match ratio</td>
    <td>LLM-as-a-Judge / EM</td>
  </tr>
</table>

Table 2: Key settings of synthetic and WILD evaluation across all four modules.

WILD. We propose the WILD (Without Intervention, Live Decoding) evaluation framework based on the standard QA evaluation protocol (Gao et al., 2024), which implements the core modules in a more realistic manner (Figure 3b): ① input: pre-fixing question with contexts like task instructions; ② generation strategy: adopting autoregressive decoding, where each output serves as input for subsequent generation; ③ output truncation: using predefined stop tokens (e.g., “.”, “\n”, and “
”) as signal to terminate generation; ④ metric: WILD supports evaluation metrics, including BERTScore (Zhang et al., 2020) and exact match (EM). Given its popularity and alignment with human judgment, we adopt LLM-as-a-Judge4 (Li et al., 2024a) as the primary metric to illustrate the framework and conduct our study. Additional metric discussions are provided in § 6.4.

Here, we use the basic QA task to instantiate the WILD evaluation framework, as our study focuses on improving the realism of evaluation, rather than increasing task complexity. Notably, our proposed framework is task-agnostic and can be easily applied to more complex scenarios, including multihop and unstructured editing.

3The code snippets of mainstream editing evaluations with teacher forcing are presented in Appendix A.3.

4Detailed prompt is provided in Appendix A.4.
<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="4">ZsRE</th>
    <th colspan="4">COUNTERFACT</th>
    <th colspan="4">QAEdit</th>
  </tr>
  <tr>
    <th colspan="2">Reliability</th>
    <th colspan="2">Generalization</th>
    <th colspan="2">Reliability</th>
    <th colspan="2">Generalization</th>
    <th colspan="2">Reliability</th>
    <th colspan="2">Generalization</th>
  </tr>
  <tr>
    <th></th>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
  </tr>
  <tr>
    <td>FT-M</td><td>1.000</td><td>0.562</td><td>0.950</td><td>0.470</td><td>1.000</td><td>0.867</td><td>0.503</td><td>0.426</td><td>1.000</td><td>0.611</td><td>0.966</td><td>0.560</td>
  </tr>
  <tr>
    <td>MEND</td><td>0.967</td><td>0.288</td><td>0.949</td><td>0.244</td><td>0.997</td><td>0.478</td><td>0.425</td><td>0.183</td><td>0.942</td><td>0.333</td><td>0.900</td><td>0.328</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.964</td><td>0.741</td><td>0.811</td><td>0.656</td><td>0.996</td><td>0.836</td><td>0.452</td><td>0.420</td><td>0.955</td><td>0.585</td><td>0.744</td><td>0.411</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.950</td><td>0.685</td><td>0.858</td><td>0.634</td><td>0.997</td><td>0.797</td><td>0.513</td><td>0.460</td><td>0.929</td><td>0.552</td><td>0.791</td><td>0.450</td>
  </tr>
  <tr>
    <td>GRACE</td><td>0.986</td><td>0.033</td><td>0.319</td><td>0.029</td><td>0.998</td><td>0.013</td><td>0.114</td><td>0.008</td><td>0.983</td><td>0.012</td><td>0.383</td><td>0.087</td>
  </tr>
  <tr>
    <td>WISE</td><td>0.999</td><td>0.139</td><td>0.973</td><td>0.081</td><td>0.999</td><td>0.521</td><td>0.612</td><td>0.104</td><td>0.998</td><td>0.216</td><td>0.877</td><td>0.122</td>
  </tr>
  <tr>
    <td>Mistral-7b</td><td>1.000</td><td>0.441</td><td>0.824</td><td>0.358</td><td>1.000</td><td>0.733</td><td>0.330</td><td>0.220</td><td>1.000</td><td>0.562</td><td>0.862</td><td>0.503</td>
  </tr>
  <tr>
    <td>MEND</td><td>0.977</td><td>0.719</td><td>0.963</td><td>0.657</td><td>0.820</td><td>0.431</td><td>0.355</td><td>0.149</td><td>0.903</td><td>0.544</td><td>0.895</td><td>0.516</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.757</td><td>0.608</td><td>0.717</td><td>0.573</td><td>0.965</td><td>0.866</td><td>0.466</td><td>0.488</td><td>0.845</td><td>0.555</td><td>0.735</td><td>0.435</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.868</td><td>0.707</td><td>0.842</td><td>0.670</td><td>0.962</td><td>0.887</td><td>0.539</td><td>0.583</td><td>0.850</td><td>0.563</td><td>0.788</td><td>0.485</td>
  </tr>
  <tr>
    <td>GRACE</td><td>0.995</td><td>0.035</td><td>0.350</td><td>0.029</td><td>1.000</td><td>0.011</td><td>0.110</td><td>0.006</td><td>0.991</td><td>0.018</td><td>0.421</td><td>0.080</td>
  </tr>
  <tr>
    <td>WISE</td><td>0.948</td><td>0.033</td><td>0.903</td><td>0.025</td><td>0.868</td><td>0.129</td><td>0.420</td><td>0.027</td><td>0.979</td><td>0.024</td><td>0.906</td><td>0.064</td>
  </tr>
  <tr>
    <td>Llama-3-8b</td><td>1.000</td><td>0.706</td><td>0.995</td><td>0.698</td><td>1.000</td><td>0.916</td><td>0.588</td><td>0.613</td><td>1.000</td><td>0.560</td><td>0.988</td><td>0.576</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.996</td><td>0.820</td><td>0.971</td><td>0.789</td><td>0.999</td><td>0.877</td><td>0.422</td><td>0.491</td><td>0.987</td><td>0.691</td><td>0.865</td><td>0.570</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.982</td><td>0.803</td><td>0.961</td><td>0.781</td><td>0.998</td><td>0.882</td><td>0.516</td><td>0.557</td><td>0.967</td><td>0.649</td><td>0.886</td><td>0.566</td>
  </tr>
  <tr>
    <td>GRACE</td><td>0.999</td><td>0.036</td><td>0.261</td><td>0.032</td><td>1.000</td><td>0.008</td><td>0.008</td><td>0.005</td><td>0.999</td><td>0.018</td><td>0.366</td><td>0.103</td>
  </tr>
  <tr>
    <td>WISE</td><td>0.859</td><td>0.091</td><td>0.825</td><td>0.075</td><td>0.807</td><td>0.212</td><td>0.508</td><td>0.075</td><td>0.910</td><td>0.121</td><td>0.876</td><td>0.138</td>
  </tr>
  <tr>
    <th>Average</th><th>0.956</th><th>0.438</th><th>0.792</th><th>0.400</th><th>0.965</th><th>0.557</th><th>0.405</th><th>0.283</th><th>0.956</th><th>0.389</th><th>0.779</th><th>0.351</th>
  </tr>
</table>

Table 3: Comparison between synthetic evaluation (syn.) and WILD evaluation (WILD). Cell background shading indicates relative performance drop from synthetic to WILD, with darker shades indicating greater decreases.

Discussion. Table 2 details the key differences between these evaluation frameworks. Previous synthetic evaluation has two types of critical limitations compared to WILD evaluation: ① over-simplification: context-free input overlooks the complexity and variability of practical queries, and match ratio rewards partial matches of incorrect answers; ② unreasonableness: teacher forcing generation and corresponding truncation to the target length leak ground truth information that should remain inaccessible during testing. These artificial settings result in a significant gap between research on editing and its practical applications.

5 Analysis on Benchmark & Evaluation

The preliminary analysis and theoretical comparison in §3 and §4 reveal a notable disparity between synthetic and WILD evaluation. To rigorously address the question raised in §3—whether the performance gap stems from differences in dataset or evaluation—we conduct systematic single-edit experiments, where each edit is independently applied to the original model from scratch.

5.1 Experimental Setup

This section outlines the experimental setup used in all subsequent experiments, unless stated otherwise. Further details are provided in Appendix A.5.

Editing Methods. To ensure comprehensive coverage, we employ six diverse and representative editing techniques across four categories: extension based (GRACE, Hartvigsen et al., 2023 and WISE, Wang et al., 2024b, both are widely adopted lifelong editing methods), fine-tuning based (FT-M, Zhang et al., 2024), meta learning (MEND, Mitchell et al., 2022), and locate-then-edit (ROME, Meng et al., 2022 and MEMIT, Meng et al., 2023). All methods are implemented using EasyEdit5. Due to the inconsistent keys implementation in ROME, we adopt its refined variant C-ROME (Yang et al., 2024b; Gupta et al., 2024a) instead.

Edited LLMs. In line with prior research (Wang et al., 2024b; Fang et al., 2025), we test three leading open-source LLMs: Llama-2-7b-chat (Touvron et al., 2023), Mistral-7b (Jiang et al., 2023), and Llama-3-8b (Meta, 2024). Greedy decoding is used for all models, aligning with prior research. Results for MEND with Llama-3-8b are excluded due to architectural incompatibility.

Editing Datasets. We employ QAEdit along with two prevalent benchmarks, ZsRE (Levy et al., 2017) and COUNTERFACT (Meng et al., 2022), for a rigorous investigation. For QAEdit, we evaluate the edited LLMs using only samples that their

5https://github.com/zjunlp/EasyEdit
<table>
  <tr>
    <th>Input</th>
    <th>FT-M</th>
    <th>ROME</th>
    <th>MEMIT</th>
    <th>GRACE</th>
    <th>WISE</th>
  </tr>
  <tr>
    <td>context-free</td>
    <td>1.000</td>
    <td>0.985</td>
    <td>0.965</td>
    <td>0.998</td>
    <td>0.908</td>
  </tr>
  <tr>
    <td>context-guided</td>
    <td>0.937</td>
    <td>0.930</td>
    <td>0.907</td>
    <td>0.412</td>
    <td>0.838</td>
  </tr>
</table>

Table 4: Reliability score for different input formats on Llama-3-8b under teacher forcing generation, truncation at ground truth length, and match ratio metric.

unedited counterparts initially answered incorrectly. This yields evaluation sets of 12,715, 10,213, 10,467 samples for Llama-2-7b-chat, Mistral-7b, and Llama-3-8b, respectively. For ZsRE and COUNTERFACT, we use their established test sets, each with 10,000 records.

5.2 Results & Analysis

The experimental results are presented in Table 3. Due to the minor side effects in single editing scenarios, the consistently strong locality results are reported in Appendix A.6.

Benchmark Perspective: QAEdit exhibits moderately lower editing reliability compared to ZsRE and CounterFact, reflecting its diverse and challenging nature as a real-world benchmark. However, this modest gap is insufficient to explain the significant discrepancy observed in our earlier analysis.

Method Perspective: ① Recent state-of-the-art methods, GRACE and WISE, exhibit the most significant decrease, with both reliability and generalization dropping below 5%. This decline mainly stems from their edited models generating erroneous information after producing the correct answers, detailed in §6.3. ② In comparison, traditional methods like FT-M and ROME exhibit superior stability and preserve a certain level of effectiveness in WILD evaluation.

Evaluation Perspective: ① Performance on each benchmark drops sharply from synthetic evaluation (~96%) to WILD evaluation (e.g., 43.8% on ZsRE and 38.9% on QAEdit), indicating that synthetic evaluation substantially overestimates the effectiveness of editing methods. ② Unlike synthetic evaluation, which reports uniformly high scores, WILD differentiates methods effectively, providing valuable insights for future research.

6 Controlled Study of Editing Evaluation

This section presents controlled experiments to systematically investigate how different module variations in synthetic evaluation (outlined in §4) contribute to performance overestimation. Due to resource and space limitations, we conduct experiments on Llama-3-8b with 3,000 randomly sampled QAEdit instances, while the findings generalize across other LLMs and datasets.

<table>
  <tr>
    <th>Generation Strategy</th>
    <th>FT-M</th>
    <th>ROME</th>
    <th>MEMIT</th>
    <th>GRACE</th>
    <th>WISE</th>
  </tr>
  <tr>
    <td>context-free, ① ground truth length, ① match ratio</td>
    <td>teacher forcing 1.000<br>autoregressive decoding 1.000</td>
    <td>0.985<br>0.967</td>
    <td>0.965<br>0.929</td>
    <td>0.998<br>0.996</td>
    <td>0.908<br>0.765</td>
  </tr>
  <tr>
    <td>context-guided, ① ground truth length, ① match ratio</td>
    <td>teacher forcing 0.937<br>autoregressive decoding 0.800</td>
    <td>0.930<br>0.851</td>
    <td>0.907<br>0.786</td>
    <td>0.412<br>0.036</td>
    <td>0.838<br>0.592</td>
  </tr>
</table>

Table 5: Reliability of different generation strategies on Llama-3-8b under two prompt strategies.

6.1 Input

This subsection empirically isolates how idealistic prompts may lead to overestimated results in synthetic evaluation. Specifically, we compare context-free prompts with real-world input formats that include task instructions, while keeping all other modules identical. Detailed prompts are provided in Appendix A.7.

Table 4 shows that incorporating task instruction degrades performance across all editing methods, with GRACE showing the most significant decline due to its weak generalization. This trend contrasts with the behavior of original Llama-3-8b, where task instructions usually improve results (Grattafiori et al., 2024). Notably, this simple instruction already causes degradation; richer or adversarial prompts would likely worsen it further. These findings reveal that using identical prompts for editing and testing in current editing evaluation, while yielding optimistic results, may fail to reflect editing effectiveness under diverse real-world inputs.

6.2 Generation Strategy

Here, we examine how teacher forcing in the generation strategy contributes to the inflated results in synthetic evaluation. We compare reliability of teacher forcing and autoregressive decoding under two distinct input formats, while keeping all other modules consistent.

As depicted in Table 5, switching from teacher forcing to autoregressive decoding consistently leads to performance degradation across all methods, with lower-performing methods exhibiting more substantial decline. The underlying reason
<table>
  <tr>
    <th>Truncation Strategy</th>
    <th>FT-M</th>
    <th>ROME</th>
    <th>MEMIT</th>
    <th>GRACE</th>
    <th>WISE</th>
  </tr>
  <tr>
    <th colspan="6">context-free, autoregressive decoding, LLM-as-a-Judge</th>
  </tr>
  <tr>
    <td>ground truth length</td>
    <td>1.000</td>
    <td>0.954</td>
    <td>0.886</td>
    <td>0.992</td>
    <td>0.700</td>
  </tr>
  <tr>
    <td>natural stop criteria</td>
    <td>0.202</td>
    <td>0.478</td>
    <td>0.461</td>
    <td>0.301</td>
    <td>0.046</td>
  </tr>
  <tr>
    <th colspan="6">context-guided, autoregressive decoding, LLM-as-a-Judge</th>
  </tr>
  <tr>
    <td>ground truth length</td>
    <td>0.751</td>
    <td>0.783</td>
    <td>0.704</td>
    <td>0.003</td>
    <td>0.482</td>
  </tr>
  <tr>
    <td>natural stop criteria</td>
    <td>0.528</td>
    <td>0.556</td>
    <td>0.529</td>
    <td>0.000</td>
    <td>0.108</td>
  </tr>
</table>

Table 6: Reliability score under different answer truncation strategies on Llama-3-8b.

<table>
  <tr>
    <th colspan="2">Meaningless Repetition</th>
  </tr>
  <tr>
    <th>Input Prompt</th>
    <td>Who got the first Nobel Prize in physics?</td>
  </tr>
  <tr>
    <th>Target Answer</th>
    <td>Wilhelm Conrad Röntgen</td>
  </tr>
  <tr>
    <th>Natural Stop</th>
    <td>Wilhelm Conrad Röntgen Wilhelm Conrad Röntgen Wilhelm Conrad Röntgen ...</td>
  </tr>
  <tr>
    <th colspan="2">Irrelevant Information</th>
  </tr>
  <tr>
    <th>Input Prompt</th>
    <td>Who was the first lady nominated member of the Rajya Sabha?</td>
  </tr>
  <tr>
    <th>Target Answer</th>
    <td>Mary Kom</td>
  </tr>
  <tr>
    <th>Natural Stop</th>
    <td>Mary Kom is the first woman boxer to qualify for the Olympics</td>
  </tr>
  <tr>
    <th colspan="2">Incorrect Information</th>
  </tr>
  <tr>
    <th>Input Prompt</th>
    <td>When does April Fools’ Day end at noon?</td>
  </tr>
  <tr>
    <th>Target Answer</th>
    <td>April 1st</td>
  </tr>
  <tr>
    <th>Natural Stop</th>
    <td>April 1st ends at noon on April 2nd</td>
  </tr>
</table>

Table 7: Examples of additionally generated content beyond ground truth length under natural stop criteria.

<table>
  <tr>
    <th>Metric</th>
    <th>FT-M</th>
    <th>ROME</th>
    <th>MEMIT</th>
    <th>GRACE</th>
    <th>WISE</th>
  </tr>
  <tr>
    <th colspan="6">context-free, autoregressive decoding, ground truth length</th>
  </tr>
  <tr>
    <td>match ratio</td>
    <td>1.000</td>
    <td>0.967</td>
    <td>0.929</td>
    <td>0.996</td>
    <td>0.765</td>
  </tr>
  <tr>
    <td>LLM-as-a-Judge</td>
    <td>1.000</td>
    <td>0.954</td>
    <td>0.886</td>
    <td>0.992</td>
    <td>0.700</td>
  </tr>
  <tr>
    <td>exact match</td>
    <td>1.000</td>
    <td>0.903</td>
    <td>0.860</td>
    <td>0.900</td>
    <td>0.646</td>
  </tr>
  <tr>
    <th colspan="6">context-guided, autoregressive decoding, ground truth length</th>
  </tr>
  <tr>
    <td>match ratio</td>
    <td>0.800</td>
    <td>0.851</td>
    <td>0.786</td>
    <td>0.036</td>
    <td>0.592</td>
  </tr>
  <tr>
    <td>LLM-as-a-Judge</td>
    <td>0.751</td>
    <td>0.789</td>
    <td>0.707</td>
    <td>0.003</td>
    <td>0.482</td>
  </tr>
  <tr>
    <td>exact match</td>
    <td>0.718</td>
    <td>0.783</td>
    <td>0.704</td>
    <td>0.003</td>
    <td>0.460</td>
  </tr>
</table>

Table 8: Reliability score derived from different metric judgments on Llama-3-8b.

for this phenomena is that teacher forcing prevents error propagation by feeding ground truth tokens as input, while autoregressive decoding allows errors to cascade. Although teacher forcing is beneficial for stabilizing LLM training, it should be avoided during testing, where ground truth is unavailable. Our results demonstrate that inappropriate use of teacher forcing in evaluation artificially elevates editing performance, especially for methods with poor real-world performance.

6.3 Output Truncation

Besides leaking ground truth tokens, teacher forcing also implicitly controls output length by aligning with ground truth length. However, this is not applicable in real-world scenarios where ground truth is unavailable. In practice, during inference, generation typically terminates based on predefined stop tokens, e.g., “
” (Gao et al., 2024). Here, we analyze these two truncation strategies by employing GPT-4o-mini as a binary judge to assess correctness (detailed in §6.4), since length discrepancies between generated and target answers preclude the use of match ratio metric.

As shown in Table 6, truncation based on natural stop criteria significantly reduces editing performance across all methods. To identify the underlying causes, we analyze the content truncated at both the ground truth length and the natural stop criteria. Our analysis reveals that, under natural stop criteria, the edited models typically generate content beyond the ground truth length, introducing meaningless repetition and irrelevant or incorrect information, as evidenced in Table 7.

These findings demonstrate that irrational truncation in synthetic evaluation masks subsequent errors that emerge in real-world scenarios, resulting in inflated performance. As shown in Table 6, although context-guided prompting enhances generation termination, it still fails to address the fundamental limitations. Such pitfalls in current approaches, overlooked by traditional evaluation, highlight the need to explore more effective ways to express edited knowledge, such as dynamic termination via token-level uncertainty.

6.4 Metric

As explained in §4, the match ratio metric could lead to inflated performance. To quantify this effect, we compare it against more rigorous factual correctness metrics, including LLM-as-a-Judge (using GPT-4o-mini) and exact match (EM). Since match ratio requires length parity with targets, we autoregressively generate sequences to target length for all metrics for fair comparison.

The results presented in Table 8 confirm that match ratio indeed overestimates the performance of edited models. Moreover, a lower match
<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="4">Llama-2-7b-chat</th>
    <th colspan="4">Mistral-7b</th>
    <th colspan="4">Llama-3-8b</th>
  </tr>
  <tr>
    <th>Reliability syn.</th>
    <th>Locality syn.</th>
    <th>Reliability WILD</th>
    <th>Locality WILD</th>
    <th>Reliability syn.</th>
    <th>Locality syn.</th>
    <th>Reliability WILD</th>
    <th>Locality syn.</th>
    <th>Reliability WILD</th>
    <th>Locality syn.</th>
    <th>Reliability WILD</th>
  </tr>
  <tr>
    <td>FT-M</td>
    <td>0.973</td>
    <td>0.531</td>
    <td>0.420</td>
    <td>0.072</td>
    <td>0.960</td>
    <td>0.454</td>
    <td>0.573</td>
    <td>0.204</td>
    <td>0.925</td>
    <td>0.229</td>
    <td>0.127</td>
    <td>0.004</td>
  </tr>
  <tr>
    <td>MEND</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>ROME</td>
    <td>0.114</td>
    <td>0.001</td>
    <td>0.028</td>
    <td>0.001</td>
    <td>0.059</td>
    <td>0.001</td>
    <td>0.052</td>
    <td>0.028</td>
    <td>0.034</td>
    <td>0.001</td>
    <td>0.020</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>MEMIT</td>
    <td>0.057</td>
    <td>0.002</td>
    <td>0.030</td>
    <td>0.000</td>
    <td>0.058</td>
    <td>0.002</td>
    <td>0.031</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>GRACE</td>
    <td>0.370</td>
    <td>0.015</td>
    <td>1.000</td>
    <td>1.000</td>
    <td>0.416</td>
    <td>0.018</td>
    <td>1.000</td>
    <td>1.000</td>
    <td>0.368</td>
    <td>0.022</td>
    <td>1.000</td>
    <td>1.000</td>
  </tr>
  <tr>
    <td>WISE</td>
    <td>0.802</td>
    <td>0.195</td>
    <td>0.676</td>
    <td>0.184</td>
    <td>0.735</td>
    <td>0.060</td>
    <td>0.214</td>
    <td>0.003</td>
    <td>0.526</td>
    <td>0.072</td>
    <td>0.743</td>
    <td>0.104</td>
  </tr>
  <tr>
    <td>Average</td>
    <td>0.386</td>
    <td>0.124</td>
    <td>0.359</td>
    <td>0.210</td>
    <td>0.494</td>
    <td>0.089</td>
    <td>0.312</td>
    <td>0.206</td>
    <td>0.371</td>
    <td>0.065</td>
    <td>0.378</td>
    <td>0.222</td>
  </tr>
</table>

Table 9: Results of sequential editing on QAEdit under synthetic evaluation (syn.) and WILD evaluation (WILD).

ratio typically indicates a smaller proportion of fully correct answers, resulting in worse performance in LLM evaluation and EM.

In this paper, we adopt LLM-as-a-Judge as the primary metric for our study, as it captures both exact and semantically equivalent responses. EM, though limited to exact matches, offers a lightweight and efficient alternative, which we refer to as WILD-em. We exclude BERTScore, as it tends to overrate factually incorrect yet semantically similar outputs.

7 (Sequential) Editing in the Wild

Although our analysis via single editing reveals limitations in synthetic evaluation, such isolated editing fails to capture the continuous, large-scale demands of editing in real-world scenarios. Therefore, we now address our primary research question: testing model editing under WILD evaluation via sequential editing, a setup that better reflects practical requirements.

7.1 Sample-wise Sequential Editing

Experimental Setup. Following established protocols (Huang et al., 2023; Hartvigsen et al., 2023), we evaluate editing methods with a batch size of 1, i.e., updating knowledge incrementally one sample at a time. We keep the same setup as in §5.1, but limit to 1000 samples per dataset, as existing methods perform significantly worse with more edits. For QAEdit, the chosen samples are incorrectly answered by all pre-edit LLMs. Given the notable side effects in sequential editing (Yang et al., 2024a), we focus on the evaluation of reliability and locality, with generalization results provided in Appendix A.8.

![Impact of batch size (BS) when editing Llama-3-8b with FT-M and MEMIT on QAEdit.](page_1012_1012_370_246.png)

Figure 4: Impact of batch size (BS) when editing Llama-3-8b with FT-M and MEMIT on QAEdit.

Results & Analysis. The results on QAEdit are shown in Table 9, with similar findings for ZsRE and COUNTERFACT in Appendix A.9. ① In WILD evaluation with sequential editing, all methods except FT-M exhibit nearly unusable performance (only 9.3% average reliability), with FT-M achieving a 40.5% average reliability. ② The gap between synthetic and WILD evaluation further confirms the evaluation issues we discussed in §6. ③ The significantly low average locality of 21.3% highlights the severe disruption to LLMs. While GRACE effectively preserves unrelated knowledge through external edit modules, it struggles with knowledge updating. ④ Notably, FT-M exhibits relatively stable reliability, as it directly optimizes model parameters at each step rather than relying on static hypernetworks or covariance matrices derived from original LLMs, thereby ensuring effective knowledge injection during sequential editing.

7.2 Mini-Batch Sequential Editing

Real-world applications often batch multiple edits together for efficient processing of high-volume demands. Moreover, Pan et al. (2024) suggest increasing batch size may alleviate the side effects of sequential editing. Thus, this section investigates whether increasing the batch size could serve as a
Figure 5 reveals two key insights: ① While the first batch exhibits high initial reliability, its performance declines sharply with subsequent editing, suggesting that later edits disrupt the knowledge injected in earlier batches. ② As editing progresses, the effectiveness of MEMIT decreases rapidly. These findings reveal the key challenges of sequential editing: **progressive loss of previously edited knowledge coupled with decreasing effectiveness in incorporating new knowledge**, highlighting that lifelong model editing is still an open challenge.

8 Conclusion and Future Works

In this paper, we present the first systematic investigation that exposes the gap between theoretical advances and practical effectiveness of model editing by real-world QA evaluation. Our proposed QAEdit benchmark and WILD evaluation demonstrate that current model editing techniques exhibit significant limitations in practical scenarios, particularly under sequential editing. Furthermore, we reveal that this significant discrepancy from previously reported results stems from unrealistic evaluation adopted in prior model editing research. Through modular analysis and extensive controlled experiments, we uncover fundamental issues in current editing evaluation that inflate reported performance. This work establishes rigorous evaluation standards for model editing and provides valuable insights that will inspire the development of more robust editing methods, ultimately enabling reliable and efficient knowledge updates in LLMs for real-world applications.

In future research, we aim to develop editing methods that can i) generalize robustly across diverse scenarios with reliable self-termination, and ii) support lifelong sequential updates while maintaining the capabilities of edited LLMs.

Limitations

We acknowledge following limitations of our work:
• This work provides an existence proof of fundamental issues of evaluation in model editing, rather than attempting an exhaustive assessment of all existing approaches and LLMs. Due to resource constraints, we focus on representative methods and LLMs to demonstrate the issues and challenges, as exhaustive testing of all approaches is neither feasible nor necessary for establishing our findings.
• Our research makes the first systematic investigation into previously overlooked evaluation issues in model editing, prioritizing the identification and analysis of these fundamental challenges rather than solution development. Our work focuses on comprehensive analysis of these issues, uncovering their root causes and providing insights into factors affecting editing effectiveness. While presenting promising directions for future research, developing solutions to these challenges remains beyond our current scope.

• Our study focuses exclusively on parameter-based editing methods, without investigating in-context learning based knowledge editing approaches which leverage external information. While these approaches may achieve superior performance on QA tasks, our primary objective is not to advocate for any particular approach, but to critically revisit current practices in the field and provide insights for future development. We believe efficient parameter-based editing approaches have their unique advantages and represent a valuable direction worth pursuing, despite current challenges in real-world applications.

Ethics Statement

Data. All data used in our research are publicly available and do not raise any privacy concerns.

AI Writing Assistance. We employ LLMs to polish our original content, focusing on correcting grammatical errors and enhancing clarity, rather than generating new content or ideas.

Acknowledgments

This work was supported by the National Key R&D Program of China (2022YFB3103700, 2022YFB3103704), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0680201), the Beijing Natural Science Foundation (4252023), and the Innovation Funding of ICT, CAS (E361120). We would also like to thank Wenyue Hua for her valuable discussions and insightful suggestions.

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Xinwei Wu, Junzhao Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, and Deyi Xiong. 2023. DEPN: Detecting and editing privacy neurons in pre-trained language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2875–2886, Singapore. Association for Computational Linguistics.

Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, and Xueqi Cheng. 2024a. The butterfly effect of model editing: Few edits can trigger large language models collapse. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5419–5437, Bangkok, Thailand. Association for Computational Linguistics.

Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Du Su, Dawei Yin, and Huawei Shen. 2024b. The fall of ROME: Understanding the collapse of LLMs in model editing. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4079–4087, Miami, Florida, USA. Association for Computational Linguistics.

Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023. Editing large language models: Problems, methods, and opportunities. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10222–10240, Singapore. Association for Computational Linguistics.

Lang Yu, Qin Chen, Jie Zhou, and Liang He. 2024. Melo: enhancing model editing with neuron-indexed dynamic lora. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence, AAAI’24/IAAI’24/EAAI’24. AAAI Press.

Ningyu Zhang, Yunzhi Yao, Bozhong Tian, and et al. 2024. A comprehensive study of knowledge editing for large language models. Preprint, arXiv:2401.01286.

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Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023. Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4862–4876, Singapore. Association for Computational Linguistics.

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A Appendix

A.1 Detailed Introduction of QA Datasets

Natural Questions (NQ) (Kwiatkowski et al., 2019) is a comprehensive question-answering (QA) dataset that contains real questions posed by users to the Google search, paired with high-quality, human-verified answers. The dataset consists of over 300,000 question-answer pairs, with each question derived from user queries on Google Search. These questions cover a wide variety of topics, ranging from fact-based inquiries to more complex, open-ended questions. The golden answers are sourced from Wikipedia pages, ensuring their accuracy and relevance. We adopt the test set of NQ, which contains 3610 samples, to construct our QAEedit benchmark.

TriviaQA (Joshi et al., 2017) is a large-scale QA dataset designed specifically for evaluating models on trivia-style question answering. It contains over 650,000 question-answer pairs sourced from trivia websites and is curated by trivia enthusiasts. These questions are often fact-based and test the model’s ability to retrieve information from large text corpora. We utilize 11,313 samples from the TriviaQA test set to construct QAEedit.

SimpleQA (Wei et al., 2024) is a challenging QA benchmark specifically designed to test fact-seeking question-answering models. It contains 4326 question-answer pairs curated by OpenAI, with an emphasis on short-form factuality. The questions in SimpleQA are concise, direct, and designed to probe factual knowledge. Unlike more general-purpose QA datasets, SimpleQA emphasizes clarity and the ability of models to provide precise, factually accurate answers. We employ all samples from SimpleQA for QAEedit construction.

<table>
  <tr>
    <th>Category</th>
    <th>Example</th>
    <th>Count</th>
  </tr>
  <tr>
    <td>Art & Culture</td>
    <td>Who wrote the song the glory of love?</td>
    <td>5277</td>
  </tr>
  <tr>
    <td>History & Politics</td>
    <td>Who wrote the first declaration of human rights?</td>
    <td>4070</td>
  </tr>
  <tr>
    <td>People & Biographies</td>
    <td>Which award did Reza Aslan receive in 2014?</td>
    <td>2188</td>
  </tr>
  <tr>
    <td>Geography & Environment</td>
    <td>What is the capital city of India?</td>
    <td>1954</td>
  </tr>
  <tr>
    <td>Science & Technology</td>
    <td>Which year was the actinic concept proposed?</td>
    <td>1829</td>
  </tr>
  <tr>
    <td>Sports & Leisure</td>
    <td>In what year did Kristin Otto retire from swimming?</td>
    <td>1807</td>
  </tr>
  <tr>
    <td>Health & Medicine</td>
    <td>Where are the cones in the eye located?</td>
    <td>771</td>
  </tr>
  <tr>
    <td>Society & Humanities</td>
    <td>Which is the ring finger for male in India?</td>
    <td>573</td>
  </tr>
  <tr>
    <td>Economics & Business</td>
    <td>When is the world consumer right day celebrated?</td>
    <td>463</td>
  </tr>
  <tr>
    <td>Others</td>
    <td>What kind of beer is �� PainGui?</td>
    <td>317</td>
  </tr>
</table>

Table 10: Statistics and examples of QAEedit, encompassing ten categories of knowledge. The underlined content represents the subjects identified by GPT-4.

5-shot examples to utilize its in-context learning capability, which can be seen in Figure 10. ② We utilize GPT-4 to paraphrase the edit prompts to obtain rephrased prompts. Considering that paraphrasing questions is easy for GPT-4, the specific instruction is straightforward and is presented in Figure 11. Furthermore, we manually reviewed some of the rephrased results and found them to be highly effective. ③ Moreover, for each sample of QAEedit, we randomly select a QA pair from the locality sets of the ZsRE dataset (Levy et al., 2017) as locality prompt and corresponding answer to assess locality.

As a result, our QAEedit benchmark encompasses ten categories of knowledge, covering mainstream topics with significant real-world impact. The statistical information and examples of each category are presented in Table 10. Although the knowledge category distribution in QAEedit appears imbalanced, with a predominance of “Art & Culture” and “History & Politics”, this distribution reflects real-world user preferences. Similar patterns are observed in mainstream editing datasets, such as ZsRE and COUNTERFACT. Therefore, this imbalance does not compromise the validity of QAEedit for examining the pitfalls of synthetic evaluation.

A.2 Construction and Statistics of QAEedit

In this section, we describe the detailed construction procedures and statistics of QAEedit.

While aforementioned QA benchmarks provide questions and answers as edit prompts and targets, they lack subjects for editing, as well as rephrased prompts and locality QA pairs to evaluate generalization and locality. To supplement the missing fields, our construction procedures encompass the following steps: ① We employ GPT-4 (gpt-4-1106-preview) to extract the subjects directly from the edit prompts. To improve the accuracy of extraction, we prompt the model with

A.3 Code of Evaluations with Teacher Forcing

As demonstrated by the code snippets in Figures 7 and 8, early model editing studies, such as ROME6 (Meng et al., 2022) and IKE7 (Zheng et al., 2023), relied on teacher forcing to evaluate the performance of edited models. As a result, subsequent works (Li et al., 2024b; Gupta et al.,

6The current latest version of ROME (May 2025) based on teacher forcing can be found at https://github.com/kmeng01/rome/blob/0874014cd9837e4365f3e6fc3f1400e f11509e04/experiments/py/eval_utils_zsre.py#L54.
7The latest IKE version (as of May 2025) with teacher forcing can be found at https://github.com/Zce1112zslx /IKE/blob/da58c842cd95628f281f474bc432a81cbdf1cfd te/ic1.py#L54.
2024c; Wang et al., 2024b; Huang et al., 2025) inadvertently inherited this flawed evaluation strategy. Similarly, as shown in Figure 9, EasyEdit8 (Wang et al., 2024c) also adopted this approach prior to our work; however, it now supports both teacher forcing and our proposed evaluation framework, enabling direct comparison.

A.4 Prompt of LLM-as-a-Judge

In light of the significant advancements in LLM-as-a-Judge (Li et al., 2024a), we employ GPT-4o-mini to perform binary judgments based on the provided questions, target answers, and generated responses. Following previous work (Wei et al., 2024), our complete prompt is presented in Figure 12.

A.5 Detailed Experimental Setup

A.5.1 Editing Methods

FT-M (Zhang et al., 2024) is an enhanced version of FT-L (Zhu et al., 2020; Meng et al., 2022). FT-L introduces an \( l_{\infty} \)-norm constraint into the fine-tuning objective to explicitly restrict the parameter changes between the original and edited models, thereby mitigating side effects on unrelated knowledge. However, FT-L deviates from the original fine-tuning objective by using only the last token’s prediction to maximize the probability of all tokens in the target sequence. To address this issue, FT-M improves upon FT-L by applying the cross-entropy loss to the target answer while masking the original text, which aligns more closely with the traditional fine-tuning objective and enhances performance.

MEND (Mitchell et al., 2022) employs a hypernetwork to learn low-rank decompositions of standard fine-tuning gradients. By disentangling gradients into learnable rank-one matrices, it achieves explicit control over parameter updates while maintaining tractable editing in LLMs.

ROME (Meng et al., 2022) identifies knowledge-critical layers in Transformer MLP modules through causal tracing analysis. It implements precise knowledge updates via rank-one matrix modification on the identified layer, guided by causal mediation effects in model outputs.

8The version of EasyEdit available at the time of our research (February 2025) is based on teacher forcing and can be found at https://github.com/jzunlp/EasyEdit/blob/8f70e77af18879ab935e06676701423d5124599c7/easyedit/evaluate/evaluate_utils.py#L112.

Please answer the question:
Q: Who got the first Nobel Prize in physics?
A:

Figure 6: The context-guided prompt for QA tasks.

MEMIT (Meng et al., 2023) extends ROME by developing cross-layer propagation analysis and coordinated parameter updates across multiple MLP layers, enabling efficient batch editing of large-scale knowledge.

GRACE (Hartvigsen et al., 2023) is a lifelong editing method that performs local corrections on streaming errors of deployed models. The approach writes new mappings into a pretrained model’s latent space, creating a discrete local codebook of edits without modifying model weights, allowing for sequential editing operations.

WISE (Wang et al., 2024b) addresses the similar challenge of sequential editing like GRACE. It employs a dual memory architecture comprising a main memory for pretrained knowledge and a side memory for edited content. The system utilizes a router to direct queries between these memories.

A.5.2 Edited LLMs

Llama-2-7b-chat (Touvron et al., 2023) is a model designed for conversational scenarios with 7 billion parameters. It excels in generating human-like responses in real-time, offering smooth and context-aware dialogue generation.

Mistral-7b (Jiang et al., 2023) is a superior pretrained base model with 7 billion parameters, outperforming Llama-2-13b on all examined benchmarks, offering strong performance while being resource-efficient. Specifically, we employ the version of Mistral-7B-v0.1.

Llama-3-8b (Meta, 2024) is a cutting-edge 8-billion-parameter model designed for diverse AI applications. It combines advanced techniques with scalability, ensuring high-quality generation for complex tasks like multi-turn dialogues, creative writing, and complex reasoning tasks.

A.5.3 Editing Datasets

ZsRE (Levy et al., 2017) is a popular dataset for Question Answering (QA), where each entry consists of a counterfactual statement derived from a factual Wikipedia page that needs to be edited.
<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="2">ZsRE</th>
    <th colspan="2">COUNTERFACT</th>
    <th colspan="2">QAEdit</th>
  </tr>
  <tr>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
  </tr>
  <tr>
    <th colspan="7">Llama-2-7b-chat</th>
  </tr>
  <tr>
    <td>FT-M</td><td>0.979</td><td>0.875</td><td>0.672</td><td>0.592</td><td>0.963</td><td>0.848</td>
  </tr>
  <tr>
    <td>MEND</td><td>0.990</td><td>0.922</td><td>0.581</td><td>0.649</td><td>0.981</td><td>0.891</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.995</td><td>0.946</td><td>0.972</td><td>0.939</td><td>0.991</td><td>0.929</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.989</td><td>0.920</td><td>0.953</td><td>0.905</td><td>0.980</td><td>0.881</td>
  </tr>
  <tr>
    <td>GRACE</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td>
  </tr>
  <tr>
    <td>WISE</td><td>1.000</td><td>0.999</td><td>0.830</td><td>0.958</td><td>1.000</td><td>0.999</td>
  </tr>
  <tr>
    <th colspan="7">Mistral-7b</th>
  </tr>
  <tr>
    <td>FT-M</td><td>0.994</td><td>0.937</td><td>0.823</td><td>0.760</td><td>0.980</td><td>0.943</td>
  </tr>
  <tr>
    <td>MEND</td><td>0.994</td><td>0.903</td><td>0.618</td><td>0.665</td><td>0.970</td><td>0.889</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.870</td><td>0.839</td><td>0.964</td><td>0.908</td><td>0.990</td><td>0.959</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.994</td><td>0.950</td><td>0.946</td><td>0.884</td><td>0.982</td><td>0.935</td>
  </tr>
  <tr>
    <td>GRACE</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td>
  </tr>
  <tr>
    <td>WISE</td><td>1.000</td><td>1.000</td><td>0.840</td><td>0.967</td><td>0.999</td><td>1.000</td>
  </tr>
  <tr>
    <th colspan="7">Llama-3-8b</th>
  </tr>
  <tr>
    <td>FT-M</td><td>0.953</td><td>0.597</td><td>0.243</td><td>0.138</td><td>0.917</td><td>0.610</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.994</td><td>0.923</td><td>0.931</td><td>0.845</td><td>0.982</td><td>0.920</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.988</td><td>0.889</td><td>0.918</td><td>0.828</td><td>0.967</td><td>0.881</td>
  </tr>
  <tr>
    <td>GRACE</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td>
  </tr>
  <tr>
    <td>WISE</td><td>0.993</td><td>0.873</td><td>0.847</td><td>0.931</td><td>0.994</td><td>0.881</td>
  </tr>
</table>

<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="2">ZsRE</th>
    <th colspan="2">COUNTERFACT</th>
    <th colspan="2">QAEdit</th>
  </tr>
  <tr>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
    <th>syn.</th>
    <th>WILD</th>
  </tr>
  <tr>
    <th colspan="7">Llama-2-7b-chat</th>
  </tr>
  <tr>
    <td>FT-M</td><td>0.906</td><td>0.480</td><td>0.723</td><td>0.394</td><td>0.932</td><td>0.461</td>
  </tr>
  <tr>
    <td>MEND</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.000</td><td>0.000</td><td>0.241</td><td>0.066</td><td>0.076</td><td>0.007</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.035</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.057</td><td>0.002</td>
  </tr>
  <tr>
    <td>GRACE</td><td>0.312</td><td>0.027</td><td>0.119</td><td>0.005</td><td>0.371</td><td>0.044</td>
  </tr>
  <tr>
    <td>WISE</td><td>0.705</td><td>0.195</td><td>0.364</td><td>0.102</td><td>0.732</td><td>0.173</td>
  </tr>
  <tr>
    <th colspan="7">Mistral-7b</th>
  </tr>
  <tr>
    <td>FT-M</td><td>0.859</td><td>0.404</td><td>0.493</td><td>0.266</td><td>0.856</td><td>0.381</td>
  </tr>
  <tr>
    <td>MEND</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.037</td><td>0.005</td><td>0.244</td><td>0.122</td><td>0.049</td><td>0.000</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.035</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.058</td><td>0.002</td>
  </tr>
  <tr>
    <td>GRACE</td><td>0.340</td><td>0.031</td><td>0.118</td><td>0.004</td><td>0.410</td><td>0.062</td>
  </tr>
  <tr>
    <td>WISE</td><td>0.697</td><td>0.015</td><td>0.326</td><td>0.043</td><td>0.699</td><td>0.065</td>
  </tr>
  <tr>
    <th colspan="7">Llama-3-8b</th>
  </tr>
  <tr>
    <td>FT-M</td><td>0.827</td><td>0.021</td><td>0.532</td><td>0.029</td><td>0.850</td><td>0.271</td>
  </tr>
  <tr>
    <td>ROME</td><td>0.079</td><td>0.017</td><td>0.430</td><td>0.019</td><td>0.020</td><td>0.000</td>
  </tr>
  <tr>
    <td>MEMIT</td><td>0.052</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td>
  </tr>
  <tr>
    <td>GRACE</td><td>0.257</td><td>0.032</td><td>0.008</td><td>0.005</td><td>0.358</td><td>0.078</td>
  </tr>
  <tr>
    <td>WISE</td><td>0.482</td><td>0.089</td><td>0.046</td><td>0.006</td><td>0.503</td><td>0.057</td>
  </tr>
</table>

Table 11: Locality of single-edit experiments under synthetic evaluation (syn.) and WILD evaluation (WILD) across various methods, LLMs, and benchmarks.

COUNTERFACT (Meng et al., 2022) is a challenging dataset curated for model editing. It contains 21,919 nonfactual statements, initially assigned low probabilities by models, and designed to encourage substantial and meaningful modifications to the original factual statements.

A.6 Locality Results of Single Editing

The locality results of single editing experiments are presented in Table 11. The results show that for almost all baselines, their locality results are very high across two evaluation frameworks, indicating that a single edit generally has little impact on the model’s general capabilities.

A.7 Detailed Practical Prompt

In Section 6.1, we prefix the target question with a common QA task instruction (Gao et al., 2024) as the input prompt, as shown in Figure 6. We aim to utilize this context-guided prompt to represent and simulate various contexts that might occur in practical applications.

A.8 Generalization of Sequential Editing

The generalization results of sequential editing experiments are presented in Table 12. Compare to Table 9, the results indicate that current editing methods exhibit worse generalization than reliability when dealing with sequential editing requests. All methods except FT-M and WISE demonstrate near-zero generalization ability under WILD evaluation, which further proves that existing editing methods cannot effectively fulfill the practical needs of continuous editing.

<table>
  <tr>
    <th>Edit Num</th>
    <th>BS 1</th>
    <th>BS 2</th>
    <th>BS 4</th>
    <th>BS 8</th>
    <th>BS 16</th>
  </tr>
  <tr>
    <td>100</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>200</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>400</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>800</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>1000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
</table>

Table 13: The reliability for sequentially editing Llama-3-8b using MEND, illustrating the impact of different batch sizes (BS) across varying numbers of edits.

methods exhibit worse generalization than reliability when dealing with sequential editing requests. All methods except FT-M and WISE demonstrate near-zero generalization ability under WILD evaluation, which further proves that existing editing methods cannot effectively fulfill the practical needs of continuous editing.

A.9 Sequential Editing on Other Datasets

The results of sequential editing on ZsRE and COUNTERFACT are presented in Table 14. These two datasets exhibit trends similar to those observed in QAEdit, including the poor practical effectiveness of existing editing methods, the inadequacy of simplified editing evaluations, and the
<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="4">Llama-2-7b-chat</th>
    <th colspan="4">Mistral-7b</th>
    <th colspan="4">Llama-3-8b</th>
  </tr>
  <tr>
    <th>Reliability<br>syn.</th>
    <th>Locality<br>WILD</th>
    <th>Reliability<br>syn.</th>
    <th>Locality<br>WILD</th>
    <th>Reliability<br>syn.</th>
    <th>Locality<br>WILD</th>
    <th>Reliability<br>syn.</th>
    <th>Locality<br>WILD</th>
  </tr>
  <tr>
    <th colspan="10">ZsRE</th>
  </tr>
  <tr>
    <td>FT-M</td>
    <td>0.935</td>
    <td>0.517</td>
    <td>0.583</td>
    <td>0.036</td>
    <td>0.925</td>
    <td>0.465</td>
    <td>0.813</td>
    <td>0.187</td>
    <td>0.879</td>
    <td>0.013</td>
    <td>0.117</td>
    <td>0.001</td>
  </tr>
  <tr>
    <td>MEND</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>ROME</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.002</td>
    <td>0.000</td>
    <td>0.044</td>
    <td>0.004</td>
    <td>0.012</td>
    <td>0.001</td>
    <td>0.087</td>
    <td>0.020</td>
    <td>0.018</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>MEMIT</td>
    <td>0.035</td>
    <td>0.000</td>
    <td>0.014</td>
    <td>0.000</td>
    <td>0.035</td>
    <td>0.000</td>
    <td>0.016</td>
    <td>0.000</td>
    <td>0.052</td>
    <td>0.000</td>
    <td>0.022</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>GRACE</td>
    <td>0.317</td>
    <td>0.025</td>
    <td>1.000</td>
    <td>1.000</td>
    <td>0.351</td>
    <td>0.031</td>
    <td>1.000</td>
    <td>1.000</td>
    <td>0.264</td>
    <td>0.033</td>
    <td>1.000</td>
    <td>1.000</td>
  </tr>
  <tr>
    <td>WISE</td>
    <td>0.756</td>
    <td>0.215</td>
    <td>1.000</td>
    <td>1.000</td>
    <td>0.742</td>
    <td>0.017</td>
    <td>0.998</td>
    <td>0.970</td>
    <td>0.514</td>
    <td>0.098</td>
    <td>1.000</td>
    <td>1.000</td>
  </tr>
  <tr>
    <th colspan="10">COUNTERFACT</th>
  </tr>
  <tr>
    <td>FT-M</td>
    <td>0.931</td>
    <td>0.592</td>
    <td>0.225</td>
    <td>0.041</td>
    <td>0.827</td>
    <td>0.538</td>
    <td>0.222</td>
    <td>0.049</td>
    <td>0.782</td>
    <td>0.080</td>
    <td>0.029</td>
    <td>0.003</td>
  </tr>
  <tr>
    <td>MEND</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.006</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
  <tr>
    <td>ROME</td>
    <td>0.370</td>
    <td>0.094</td>
    <td>0.093</td>
    <td>0.000</td>
    <td>0.265</td>
    <td>0.131</td>
    <td>0.009</td>
    <td>0.005</td>
    <td>0.484</td>
    <td>0.022</td>
    <td>0.034</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>MEMIT</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.056</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
    <td>0.000</td>
  </tr>
  <tr>
    <td>GRACE</td>
    <td>0.153</td>
    <td>0.017</td>
    <td>0.996</td>
    <td>1.000</td>
    <td>0.148</td>
    <td>0.006</td>
    <td>0.996</td>
    <td>1.000</td>
    <td>0.012</td>
    <td>0.006</td>
    <td>0.996</td>
    <td>1.000</td>
  </tr>
  <tr>
    <td>WISE</td>
    <td>0.797</td>
    <td>0.296</td>
    <td>0.340</td>
    <td>0.522</td>
    <td>0.595</td>
    <td>0.119</td>
    <td>0.196</td>
    <td>0.081</td>
    <td>0.158</td>
    <td>0.027</td>
    <td>0.621</td>
    <td>0.912</td>
  </tr>
</table>

Table 14: Results of sequential editing on ZsRE and COUNTERFACT under synthetic evaluation (syn.) and WILD evaluation (WILD) across various editing methods and LLMs.

dilemma of achieving editing success and preserving unrelated knowledge.

A.10 Mini-Batch Sequential Editing of MEND
As shown in Table 13, unlike FT-M and MEMIT, which maintain a certain level of editing performance under specific batch sizes (as depicted in Figure 4), MEND is completely unusable for sequential editing, regardless of the batch size. This ineffectiveness can be attributed to the limitation of the meta-learning paradigm, wherein the hypernetwork for parameter updates is specifically trained on the original model. Consequently, the predicted parameter modifications are optimized solely for the original model and fail to effectively adapt to the evolving states of the sequentially edited model. This limitation fundamentally constrains MEND’s efficacy in sequential editing scenarios.
inp_prompts = [
    el + tok.decode(target_tok[:i])
    for el in inp_prompts_og
    for i in range(len(target_tok))
]
inp_targets = [--
]

stuff_probs = test_batch_prediction_acc(model, tok, inp_prompts, inp_targets)

# Predict for neighborhood prompts (dictionary format).
neighborhood_correct = test_batch_prediction_acc(--
)

probs = stuff_probs + neighborhood_correct

# Unflatten the results again into a list of lists.
cutoffs = [0] + np.cumsum(--
).tolist()
ret_probs = [probs[cutoffs[i - 1] : cutoffs[i]] for i in range(1, len(cutoffs))]
# Structure the restuls as a dictionary.
ret = {--
}
ret["neighborhood_prompts_correct"] = neighborhood_correct

return ret

def test_batch_prediction_acc(model, tok, prompts: typing.List[str], target):
    prompt_tok = tok(
        prompts,
        padding=True,
        return_tensors="pt",
    ).to("cuda")

    with torch.no_grad():
        logits = model(**prompt_tok).logits

Figure 7: Code from ROME illustrating teacher forcing evaluation, where target answers (target_tok) are incorporated into input prompts (inp_prompts_og) for generation.

def icl_lm_eval(model, tokenizer, icl_examples, targets, x):
    ppls = []
    for target in targets:
        tgt_len = len(tokenizer.encode(' ' + target))
        encodings = tokenizer(''.join(icl_examples) + f'{x} {target}', return_tensors='pt')
        input_ids = encodings['input_ids'].to(device)
        target_ids = input_ids.clone()
        target_ids[:, :-tgt_len] = -100
        with torch.no_grad():
            outputs = model(input_ids, labels=target_ids)
            ppl = torch.exp(outputs.loss)
            ppls.append(ppl.item())
    return ppls

Figure 8: Code from IKE demonstrating teacher forcing evaluation. Similarly, the target answers (target) are placed after the in-context demonstrations (icl_examples) and input prompts (x) for generation.
prompt_target = [prompt + ' ' + target for prompt, target in zip(prompts,targets)]
max_prompt_len = max([len(tok.encode(_)) for _ in prompt_target]) + 1
before_padding_side = tok.padding_side
tok.padding_side = 'left'
prompt_target_tok = tok(
    prompt_target,
    padding=True,
    truncation=True,
    max_length=max(hparams.max_length, max_prompt_len),
    return_tensors="pt",
).to(f"cuda:{device}")
prompt_tok = tok(
    prompts,
    padding=True,
    truncation=True,
    max_length=max(hparams.max_length, max_prompt_len),
    return_tensors="pt",
)
tok.padding_side = before_padding_side
num_prompt_toks = [int(i != tok.pad_token_id).sum() for i in prompt_tok['input_ids']]
num_pad_toks = [int(i == tok.pad_token_id).sum() for i in prompt_target_tok['input_ids'].cpu()]
prompt_len = [x+y for x,y in zip(num_pad_toks,num_prompt_toks)]
with torch.no_grad():
    outputs = model(**prompt_target_tok)

Figure 9: Code from EasyEdit where the target answers (targets) are appended to the input prompts (prompts) for teacher forcing generation.

Prompt for Subject Extraction

Please identify the subject in the provided prompt and respond solely with the subject, ensuring the subject is directly drawn from the prompt itself (including the need for exact match in case, both uppercase and lowercase).

Here are some examples for guidance:

{'prompt': 'Who published Journal of Clinical Microbiology?', 'subject': 'Journal of Clinical Microbiology'}
{'prompt': 'Who was mainly responsible for the design of Abney Park Chapel?', 'subject': 'Abney Park Chapel'}
{'prompt': 'Who was behind the creation of IAC Building?', 'subject': 'IAC Building'}
{'prompt': "Who is Li Jiancheng's sister?", 'subject': 'Li Jiancheng'}
{'prompt': "Who is the Haitz's law named after?", 'subject': "Haitz's law"}

Based on the examples, for 'prompt': 'Who got the first Nobel Prize in physics?', the 'subject' is:

Figure 10: Complete prompt used for directly extracting subject from edit prompt for QAEdit.

Prompt for Question Paraphrasing

Role and Goal: Serves as a data engineer, use your knowledge to rewrite the following question in a different way, ensuring it conveys the same meaning and maintains a neutral tone but with different wording. Avoid using phrases such as 'Could you tell me'. Instead, directly rephrase it into a structured question.

Please rephrase the following question: Who got the first Nobel Prize in physics?

Figure 11: Complete prompt for paraphrasing edit question into rephrased question for generalization evaluation.
Prompt for LLM-as-a-Judge

Your job is to look at a question, a gold target, and a predicted answer, and then assign a grade → of either ["CORRECT", "INCORRECT"].

The following are examples of CORRECT predicted answers.
```
Question: What are the names of Barack Obama's children?
Gold target: Malia Obama and Sasha Obama
Predicted answer 1: sasha and malia obama
Predicted answer 2: Malia and Sasha Obama are the names of Barack Obama's children.
```
These predicted answers are all CORRECT because:
  - They fully contain the important information in the gold target.
  - They do not contain any information that contradicts the gold target.

The following are examples of INCORRECT predicted answers.
```
Question: What are the names of Barack Obama's children?
Gold target: Malia and Sasha
Predicted answer 1: Malia.
Predicted answer 2: Malia, Sasha, and Susan.
Predicted answer 3: Malia and Sasha, Malia and Sasha, Malia and Sasha, Malia and Sasha (repeated answer)
```
These predicted answers are all INCORRECT because:
  - A factual statement in the answer contradicts the gold target or contain repeated answer.

Here is a sample. Simply reply with either CORRECT or INCORRECT.
```
Question: {question}
Gold target: {target}
Predicted answer: {predicted_answer}
```

According to the gold target, please grade the predicted answer of this question as one of:
A: CORRECT
B: INCORRECT

Just return the letters "A" or "B", with no text around it.

Figure 12: The complete prompt used to employ a LLM as a judge for providing binary assessments (correct or incorrect) based on a given question, gold target answer, and predicted answer.

Reviewer paper summaries: This paper presents QAEdit, which addresses the problem that using teacher forcing for knowledge editing is unrealistic. To tackle this, the authors design real-world editing modules across input, generation strategy, output truncation, and evaluation metrics. Experiments with QAEdit show that existing editing methods fall short in these real-world editing settings, exhibiting substantial performance degradation.
Summary of strengths: 1. The development of QAEdit, which newly addresses the reliance on teacher forcing in existing editing tasks, is both interesting and well-motivated.
2. The experimental results demonstrate that popular editing methods suffer substantial performance degradation on QAEdit, providing valuable insights to the literature.
Summary of weaknesses: 1. As a major issue, while the editing methods considered in the paper are limited to locate-then-edit and memory-based approaches, other methods such as in-context editing and RAG-style editing have not been explored.
2. It is not fully convincing that the proposed editing settings encompass most major real-world evaluation scenarios. More discussion about the coverage of the editing settings needs to be presented. In addition, it would be helpful to discuss how the proposed framework could be extended to handle multi-hop editing situations or other general cases.
3. While these results of the paper are valuable, a deeper discussion is needed to explain why existing editing methods fail under the proposed settings.
Comments, suggestions and typos: Please see weaknesses.
Overall assessment scores: 3.5
Metareview score: 4
Metareview: This paper introduces QAEdit, a benchmark designed for systematic analysis of model editing methods. The experiments highlight a significant discrepancy between current reported performance metrics and real-world scenarios. The results underscore the urgent need for more rigorous evaluation approaches in model editing research.
Summary of reasons to publish: Reviewers identified several key strengths of the paper, including:
1/ The proposed QAEdit benchmark is timely, novel, and effective.
2/ The paper is clearly written, with detailed and precise technical descriptions.
3/ The experimental design is thorough, covering six editing techniques across four categories. The results validate the hypothesis of performance degradation when evaluated with QAEdit, providing critical insights and contributing to higher evaluation fidelity in model editing research.