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Towards Evaluation for Real-World LLM Unlearning
Ke Miao, Yuke Hu, Xiaochen Li, Wenjie Bao, Zhihao Liu, Zhan Qin, Kui Ren
Models: LLaMA2-7B, Phi-1.5B, Qwen2.5-7B
Intelligence
Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 95%
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Summary
The paper introduces Distribution Correction-based Unlearning Evaluation (DCUE), a novel metric for assessing LLM unlearning. DCUE addresses the limitations of existing metrics—specifically practicality, exactness, and robustness—by using a validation set to correct distributional biases in core token confidence scores and employing the Kolmogorov-Smirnov test to ensure stability against post-processing operations.
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DCUE → usesmethod → Kolmogorov-Smirnov test
confidence 100% · it uses a combination of aforementioned designs with the Kolmogorov–Smirnov test
DCUE → addresseslimitationsof → Existing Unlearning Evaluation Metrics
confidence 95% · Experimental results demonstrate that DCUE overcomes the limitations of existing metrics
LLM → undergoes → Unlearning
confidence 90% · many unlearning methods have been proposed
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Abstract
Abstract:This paper analyzes the limitations of existing unlearning evaluation metrics in terms of practicality, exactness, and robustness in real-world LLM unlearning scenarios. To overcome these limitations, we propose a new metric called Distribution Correction-based Unlearning Evaluation (DCUE). It identifies core tokens and corrects distributional biases in their confidence scores using a validation set. The evaluation results are quantified using the Kolmogorov-Smirnov test. Experimental results demonstrate that DCUE overcomes the limitations of existing metrics, which also guides the design of more practical and reliable unlearning algorithms in the future.
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- Source: https://arxiv.org/abs/2508.01324
- Canonical: https://arxiv.org/abs/2508.01324
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Towards Evaluation for Real-World LLM Unlearning Ke Miao1,2, Yuke Hu1,2, Xiaochen Li3, Wenjie Bao1,2, Zhihao Liu1,2, Zhan Qin1,2,🖂, Kui Ren1,2 1The State Key Laboratory of Blockchain and Data Security, Zhejiang University 2Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, 3UNC Greensboro miaoke, yukehu, wenjie_\__bao, zhihao_\__liu, qinzhan, kuiren@zju.edu.cn X_\__LI12@uncg.edu Abstract This paper analyzes the limitations of existing unlearning evaluation metrics in terms of practicality, exactness, and robustness in real-world LLM unlearning scenarios. To overcome these limitations, we propose a new metric called Distribution Correction-based Unlearning Evaluation (DCUE). It identifies core tokens and corrects distributional biases in their confidence scores using a validation set. The evaluation results are quantified using the Kolmogorov–Smirnov test. Experimental results demonstrate that DCUE overcomes the limitations of existing metrics, which also guides the design of more practical and reliable unlearning algorithms in the future. †footnotetext: 🖂 Corresponding author. 1 Introduction Large language models (LLMs) are widely applied across various domains such as medical diagnosis, financial forecasting, education, and legal document analysis (Thirunavukarasu et al., 2023; Li et al., 2023; Xiao et al., 2023; Fei et al., 2023). Their training relies heavily on large-scale datasets and significant computational resources. As a result, developers often start with open-source pretrained models and fine-tune them on datasets in specific fields to obtain customized LLMs. These datasets may contain sensitive information (Carlini et al., 2021; Henderson et al., 2023; Min et al., 2023; He et al., 2024). When such models are deployed for specific tasks, data owners may later request that certain sensitive data be “forgotten” by the model. This need has attracted significant attention from the research community, and many unlearning methods have been proposed (Ginart et al., 2019; Liu et al., 2020; Wu et al., 2020; Bourtoule et al., 2021; Izzo et al., 2021; Gupta et al., 2021; Sekhari et al., 2021; Ghazi et al., 2023; Hu et al., 2024b; Lu et al., 2022; Kumar et al., 2022; Ilharco et al., 2023; Zhang et al., 2023; Wang et al., 2024; Yu et al., 2023; Pawelczyk et al., 2023; Ishibashi and Shimodaira, 2024; Chen and Yang, 2023; Wu et al., 2023; Patil et al., 2023; Thaker et al., 2024). However, apart from relying on the developer’s promise, it remains a challenge for data owners to verify whether the unlearning has actually been carried out. To address this challenge, several evaluation metrics have been proposed to help verify whether a model has effectively performed the unlearning task (Shi et al., 2024; Jin et al., 2024; Maini et al., 2024; Li et al., 2024). These metrics evaluate the unlearned model from different perspectives, including text similarity, multiple-choice accuracy, prediction probability and membership inference attack (MIA). However, in practical settings, these metrics are unreliable, with significant limitations in terms of practicality, exactness, and robustness. First, practicality refers to the ability of the metric to effectively evaluate without using the retrained model. Existing metrics such as prediction probability-based (Maini et al., 2024) and MIA-based (Shi et al., 2024) require a retrained model as a gold standard. However, the retrained model is typically inaccessible in real-world unlearning evaluation scenarios. If it is accessible, it would naturally satisfy the unlearning requirements without the need for additional unlearning procedures. Second, exactness refers to the ability of the metric to assign a score that accurately reflects the degree of unlearning. Text similarity-based metrics (Shi et al., 2024; Jin et al., 2024) are skewed by non-critical tokens. For instance, models retaining sensitive knowledge may receive lower Rouge-L scores than truly unlearned models (Figure 2). Multiple-choice accuracy-based (Li et al., 2024) metrics are vulnerable to LLMs’ reasoning capabilities, where models can guess correct answers without memorization (Figure 2). Third, robustness refers to the ability of the metric to maintain stable results when the unlearned model undergoes a series of post-processing operations. Post-processing operations refer to tasks that do not involve the forget dataset, such as unlearning other data samples or fine-tuning on a new dataset. Most current metrics are sensitive to post-processing operations. As a result, it’s difficult to reasonably evaluate the model using existing metrics when the model is frequently updated. Figure 1: Example illustrating the limitation of Evaluation based on Text Similarity. Figure 2: Example illustrating the limitation of Evaluation based on Multiple-Choice Accuracy. To overcome these limitations, we propose a novel evaluation metric, Distribution Correction-based Unlearning Evaluation (DCUE). DCUE introduces three key innovations corresponding to the aforementioned limitations. First, it eliminates reliance on a retrained model by leveraging the original open-source model and a validation dataset to correct the characteristic difference between the open-source model and retrained model. This ensures practicality without requiring computationally intensive retraining. Second, DCUE focuses on core token confidence scores instead of the full output sequence, filtering out irrelevant token-level noise to enhance exactness. Third, it uses a combination of aforementioned designs with the Kolmogorov–Smirnov test (KS-Test) (An, 1933; Smirnoff, 1939) to ensure evaluation robustness, resisting misleading effects from post-processing operations. The KS-Test measures the maximum difference between the Empirical Cumulative Distribution Functions (ECDFs) of two sample distributions, providing a way to check if the two distributions are significantly different. Our experiments validate that DCUE achieves superior practicality, exactness, and robustness compared to existing metrics across multiple LLM architectures and datasets. We further apply DCUE to evaluate several existing unlearning methods. The results reveal their limited effectiveness, highlighting the need for future improvements in unlearning algorithm design. Our contributions are summarized as follows: • We point out that the existing metrics are unreliable and identify their limitations in terms of practicality, exactness, and robustness. • We design a new metric DCUE which addresses the challenges faced by the existing metrics. • Extensive experiments demonstrate that DCUE significantly outperforms existing metrics. • DCUE reveals that existing unlearning algorithms still have room for improvement and offers guidance for future designs. 2 Problem Formulation Let MoM_oMitalic_o denote the original open-source foundation model (e.g., LLaMA). MoM_oMitalic_o undergoes fine-tuning on a private dataset DtD_tDitalic_t to produce task-specific model MtM_tMitalic_t. When privacy or regulatory requirements necessitate the removal of a sensitive subset Df⊆DtD_f D_tDitalic_f ⊆ Ditalic_t, we apply unlearning procedures to obtain the modified model MuM_uMitalic_u. As the pretraining dataset generally contains publicly available data, it rarely triggers deletion requests. The private DtD_tDitalic_t is the typical source of sensitive or proprietary data requiring unlearning in practical applications. Therefore, our work emphasizes the unlearning evaluation of fine-tuning data to meet real-world demands. Formally, we consider Df=(qi,ai)i=1nD_f=\(q_i,a_i)\_i=1^nDitalic_f = ( qitalic_i , aitalic_i ) i = 1n as a collection of question-answer pairs requiring deletion, where n=|Df|n=|D_f|n = | Ditalic_f | denotes the forget dataset size. The goal is to evaluate MuM_uMitalic_u using an appropriate metric. Ideally, MuM_uMitalic_u should be compared to a model MrM_rMitalic_r, which is retrained from MoM_oMitalic_o on the retained dataset Dr=Dt∖DfD_r=D_t D_fDitalic_r = Ditalic_t ∖ Ditalic_f. However, the MrM_rMitalic_r is typically inaccessible in the real-world unlearning evaluation setting. We denote the evaluation outcome as RevalR_evalReval. Our objective is to develop an evaluation metric that enables RevalR_evalReval to assess the unlearning effectiveness of MuM_uMitalic_u accurately and reliably in real-world deployment scenarios without access to MrM_rMitalic_r. 3 Blueprint of Ideal Evaluation Metric 3.1 Properties of Ideal Metrics In this section, we systematically analyze the properties that an ideal unlearning evaluation metric should possess in real-world scenarios: practicality, exactness, and robustness. Practicality determines whether the metric can be applied to real-world settings. Exactness determines whether the metric can reasonably reflect the degree of forgetting. Robustness determines whether the metric can cope with deployment scenarios where the model will be frequently updated. Next, we will elaborate on each of these properties in detail. The overall structural diagram is presented in Figure 3. Figure 3: Blueprint of ideal metric in real-world settings. It contains three key properties: Practicality ensures applicability without MrM_rMitalic_r. Exactness ensures the true degree of unlearning. Robustness ensures stability under frequent model updates. Each property is quantified by normalized score. Evaluation Practicality. Evaluation practicality refers to the ability of an evaluation metric to effectively evaluate MtM_tMitalic_t without using MrM_rMitalic_r. In the context of unlearning evaluation, MrM_rMitalic_r is the model obtained by retraining on DrD_rDitalic_r and is often used as the gold standard for unlearning evaluation. However, the MrM_rMitalic_r is inaccessible in real-world unlearning evaluation scenarios. If MrM_rMitalic_r is accessible, it would intrinsically fulfill all unlearning objectives without the need for unlearning procedures. Therefore, independence from MrM_rMitalic_r is a fundamental factor for an evaluation metric to be applicable in practical scenarios. Evaluation Exactness. Evaluation exactness refers to the ability of an evaluation metric to assign a score that accurately reflects the degree of unlearning. An ideal metric should give MrM_rMitalic_r the highest score, as it achieves the theoretical optimal level of forgetting, i.e., being completely unaffected by DfD_fDitalic_f. We term it positive exactness, denoted as exactness+exactness^+e x a c t n e s s+. Conversely, the ideal metric should assign the lowest score to MtM_tMitalic_t, as it has not undergone any unlearning. We term it negative exactness, denoted as exactness−exactness^-e x a c t n e s s-. We use the following formula to quantify the exactness of unlearning evaluation metrics: exactness+=1−|fe(Mr,Df)−Reval+|range(Reval)exactness−=1−|fe(Mt,Df)−Reval−|range(Reval) \ matrixexactness^+=1- |f_e(M_r,D_f)-R_eval^+|range(R_eval)\\ \\ exactness^-=1- |f_e(M_t,D_f)-R_eval^-|range(R_eval) matrix . start_ARG start_ROW start_CELL e x a c t n e s s+ = 1 - divide start_ARG | fitalic_e ( Mitalic_r , Ditalic_f ) - Ritalic_e v a l+ | end_ARG start_ARG r a n g e ( Ritalic_e v a l ) end_ARG end_CELL end_ROW start_ROW start_CELL end_CELL end_ROW start_ROW start_CELL e x a c t n e s s- = 1 - divide start_ARG | fitalic_e ( Mitalic_t , Ditalic_f ) - Ritalic_e v a l- | end_ARG start_ARG r a n g e ( Ritalic_e v a l ) end_ARG end_CELL end_ROW end_ARG (1) where Reval+R_eval^+Ritalic_e v a l+ and Reval−R_eval^-Ritalic_e v a l- represent the theoretical optimal and worst-case values respectively. range(Reval)range(R_eval)r a n g e ( Ritalic_e v a l ) denotes the scale of the evaluation metric. Evaluation Robustness. Evaluation robustness refers to the ability of an evaluation metric to maintain stable results even after MuM_uMitalic_u undergoes a series of post-processing operations. The post-processing operations are independent of DfD_fDitalic_f, including the following three operations: PostPro_ulPostProbold_ul, PostPro_ftPostProbold_ft, PostPro_mixPostProbold_mix. • :PostPro_ul:PostProbold_ul : MuM_uMitalic_u undergoes unlearning on Df′D_f^ Ditalic_fstart_FLOATSUPERSCRIPT ′ end_FLOATSUPERSCRIPT, where Df′∈DtD_f^ ∈ D_tDitalic_fstart_FLOATSUPERSCRIPT ′ end_FLOATSUPERSCRIPT ∈ Ditalic_t and Df′∩Df=∅D_f^ ∩ D_f= _fstart_FLOATSUPERSCRIPT ′ end_FLOATSUPERSCRIPT ∩ Ditalic_f = ∅. • :PostPro_ft:PostProbold_ft : MuM_uMitalic_u undergoes fine-tuning on DuD_uDitalic_u, where Du∩Df=∅D_u∩ D_f= _u ∩ Ditalic_f = ∅. • :PostPro_mix:PostProbold_mix : MuM_uMitalic_u undergoes both unlearning on Df′D_f^ Ditalic_fstart_FLOATSUPERSCRIPT ′ end_FLOATSUPERSCRIPT and fine-tuning on DuD_uDitalic_u. A robust unlearning evaluation metric should yield consistent evaluation results for the post-processed models. For x∈[ul,ft,mix]x∈[ul,ft,mix]x ∈ [ u l , f t , m i x ], we use the following formula to quantify the robustness of unlearning metric: robustnessx=1−|fe(PostProx(Mu),Df)−fe(Mu,Df)|range(Reval)robustness_x=1- |f_e(PostPro_x(M_u),D_f)-f_e(M_u,D_f)|range(R_eval)r o b u s t n e s sitalic_x = 1 - divide start_ARG | fitalic_e ( P o s t P r oitalic_x ( Mitalic_u ) , Ditalic_f ) - fitalic_e ( Mitalic_u , Ditalic_f ) | end_ARG start_ARG r a n g e ( Ritalic_e v a l ) end_ARG (2) 3.2 Existing Metrics are Not Ideal Existing research has proposed several unlearning evaluation metrics. Their evaluations are based on different aspects of the model’s performance, including Text Similarity, Multiple-Choice Accuracy, Prediction Probability and MIA. We summarize the existing metrics and their intuitive limitations in Table 1. In addition, we also quantitatively verify the limitations of existing metrics with experiments in Section 5.1. Next, we will introduce each indicator in detail and explain its limitations. Table 1: Summary of Existing LLM Unlearning Evaluation Metrics. The limitations column summarizes the main limitations of each category. Bolded items in limitations denote obvious limitations, while non-bolded items represent implicit limitations. Category Metric Formula Mechanism Limitations Text Sim. QA ((Jin et al., 2024)) 1|Df|∑Rouge-Lr(Mu(q),a) 1|D_f|Σ Rouge -L_r(M_u(q),a)divide start_ARG 1 end_ARG start_ARG | Ditalic_f | end_ARG ∑ R o u g e - Litalic_r ( Mitalic_u ( q ) , a ) Compare responses with original answers exactness, robustness FB ((Jin et al., 2024)) 1|FB(Df)|∑Rouge-Lr(Mu(qfb),a) 1|FB(D_f)|Σ Rouge -L_r(M_u(q_fb),a)divide start_ARG 1 end_ARG start_ARG | F B ( Ditalic_f ) | end_ARG ∑ R o u g e - Litalic_r ( Mitalic_u ( qitalic_f b ) , a ) Convert QA to fill-in-the-blank format exactness, robustness A ((Jin et al., 2024)) 1|AA(Df)|∑Rouge-Lr(Mu(qadv),a) 1|A(D_f)|Σ Rouge -L_r(M_u(q_adv),a)divide start_ARG 1 end_ARG start_ARG | A A ( Ditalic_f ) | end_ARG ∑ R o u g e - Litalic_r ( Mitalic_u ( qitalic_a d v ) , a ) Use adversarial jailbreak prompts exactness, robustness VerbMem ((Shi et al., 2024)) 1|Df|∑Rouge-Lf(Mu(x[:l]),x[l+1:]) 1|D_f|Σ Rouge -L_f(M_u(x[:l]),x[l+1:])divide start_ARG 1 end_ARG start_ARG | Ditalic_f | end_ARG ∑ R o u g e - Litalic_f ( Mitalic_u ( x [ : l ] ) , x [ l + 1 : ] ) Measure continuation similarity after prefix exactness, robustness KnowMem ((Shi et al., 2024)) 1|Df|∑Rouge-Lf(Mu(q),a) 1|D_f|Σ Rouge -L_f(M_u(q),a)divide start_ARG 1 end_ARG start_ARG | Ditalic_f | end_ARG ∑ R o u g e - Litalic_f ( Mitalic_u ( q ) , a ) Direct answer similarity assessment exactness, robustness Mul. Acc. QA Eval ((Li et al., 2024)) 1|CA(Df)|∑Acc(Mu(qmc),a) 1|CA(D_f)|Σ Acc(M_u(q_mc),a)divide start_ARG 1 end_ARG start_ARG | C A ( Ditalic_f ) | end_ARG ∑ A c c ( Mitalic_u ( qitalic_m c ) , a ) Accuracy on multiple-choice conversions exactness, robustness Prob Eval ((Li et al., 2024)) 2|CA(Df)|∑Acc(PE(Mu)(q),a) 2|CA(D_f)|Σ Acc(PE(M_u)(q),a)divide start_ARG 2 end_ARG start_ARG | C A ( Ditalic_f ) | end_ARG ∑ A c c ( P E ( Mitalic_u ) ( q ) , a ) Fine-tune MuM_uMitalic_u on half DfD_fDitalic_f, test on remainder exactness, robustness Pred. Prob. TR Eval ((Maini et al., 2024)) KS(TR(Mr),TR(Mu))KS(TR(M_r),TR(M_u))K S ( T R ( Mitalic_r ) , T R ( Mitalic_u ) ) KS-Test on truth ratio distributions practicality, robustness MIA PrivLeak ((Shi et al., 2024)) AUC(Mu)−AUC(Mr)AUC(Mr) AUC(M_u)-AUC(M_r)AUC(M_r)divide start_ARG A U C ( Mitalic_u ) - A U C ( Mitalic_r ) end_ARG start_ARG A U C ( Mitalic_r ) end_ARG MIA via Min-K% Prob practicality, robustness Metrics based on Text Similarity assess the effectiveness of unlearning by comparing the generated text from MuM_uMitalic_u with reference answers (Jin et al., 2024; Shi et al., 2024). They often employ metrics such as Rouge scores. Variants of this approach include converting questions into fill-in-the-blank formats or using adversarial prompts to test the model’s memorization degree. Despite their intuitive design, these metrics suffer from fundamental limitations in exactness. Specifically, they are highly sensitive to non-critical tokens that do not contribute meaningfully to the semantic correctness of answers. As exemplified in Figure 2, Mu1M_u1Mitalic_u 1 answers correctly. This indicates that the model still retains memory of the knowledge. Mu2M_u2Mitalic_u 2 does not answer correctly, suggesting that the model may have forgotten the knowledge. Nevertheless, the Rouge-L score between Mu1(Q)M_u1(Q)Mitalic_u 1 ( Q ) and the A is lower than that between Mu2(Q)M_u2(Q)Mitalic_u 2 ( Q ) and the A. The evaluation result implies that Mu1M_u1Mitalic_u 1’s level of unlearning is superior to Mu2M_u2Mitalic_u 2’s, which contradicts the actual situation. Metrics based on Multiple-Choice Accuracy convert the forget dataset into multiple-choice questions and assess how close the accuracy after unlearning is to random chance (Li et al., 2024). This metric is naturally suited for classification tasks, as it directly evaluates whether the model selects the correct answer from a set of discrete options. However, its application to large language models (LLMs) introduces critical challenges. Since the original data rarely exists in multiple-choice format, distractor options must be artificially created. This design makes the evaluation outcome highly sensitive to the quality and construction of these options. This leads to limitations in exactness during the evaluation process. Overly simplistic or excessively ambiguous distractors can skew results, either inflating or deflating accuracy measures. As shown in Figure 2, even if MuM_uMitalic_u has completely unlearned the relevant knowledge, it may still select the correct answer based on general reasoning ability. Consequently, for LLMs, even complete unlearning of DfD_fDitalic_f does not guarantee that accuracy on CA(Df)CA(D_f)C A ( Ditalic_f ) will converge to random chance. Metrics based on Prediction Probability and MIA evaluate unlearning by analyzing distribution shifts or privacy leakage (Maini et al., 2024; Shi et al., 2024). They often employ statistical tests and differential AUC-ROC scores to compare predictions between MuM_uMitalic_u and MrM_rMitalic_r, quantifying the residual memorization of target data. While theoretically rigorous, existing metrics based on both Prediction Probability and MIA share a critical dependency on access to MrM_rMitalic_r as a ground truth baseline as shown in the last two rows of Table 1. This leads to limitations in its practicality during the evaluation process. In real-world unlearning evaluation scenarios, the MrM_rMitalic_r is inaccessible, otherwise unlearning is unnecessary. Without this gold-standard reference, their evaluation cannot be fully realized, constraining their practicality despite their theoretical appeal. 4 Our Method In this section, we propose a new metric called Distribution Correction-based Unlearning Evaluation (DCUE). The overall structure of this section is as follows: Section 4.1 presents a comprehensive overview of DCUE, Sections 4.2 to 4.4 describe the details of each operational step, and Section 4.5 verifies the rationality of the approximation strategy in DCUE. 4.1 DCUE: An Overview Addressing the Limitations of Non-Critical Tokens and Problem Design Sensitivity. To mitigate the limitations arising from non-critical tokens and the sensitivity of problem design in Text Similarity and Multiple-Choice Accuracy-based evaluations, we propose Core Token Confidence Scores (CTCS). For each question-answer pair in the dataset, given a question, the model outputs the probability assigned to each token in the ground-truth answer, forming a sequence referred to as Token Confidence Scores (TCS). We further extract core tokens, defined as the minimal subset of tokens that play a decisive role in answering the given question. By retaining only the confidence scores corresponding to these core tokens, we obtain CTCS, which directly reflects the model’s retention of key knowledge points relevant to the specified data. Addressing the Limitation of Retrained Model Dependency. To overcome the reliance on the retrained model MrM_rMitalic_r inherent in Prediction Probability and MIA-based evaluations, we leverage the publicly accessible original open-source model MoM_oMitalic_o. Additionally, we introduce a validation dataset DvD_vDitalic_v, which approximates the distribution of the training dataset DtD_tDitalic_t, to correct for distributional drift caused by the retained portions of the fine-tuning data. DvD_vDitalic_v is sampled from the fine-tunable dataset, and it needs to ensure that the data in DvD_vDitalic_v does not participate in the fine-tuning process of the target forgetting model. Specifically, the validation dataset is required to satisfy two conditions: (1) not included in the forget dataset DfD_fDitalic_f, and (2) not involved in model fine-tuning training. Notably, DCUE does not require the distribution of DvD_vDitalic_v to strictly match that of the training data. The workflow of DCUE is shown in Figure 4. We will detail each step in the following subsections. Figure 4: The workflow of DCUE. It first extracts CTCS of MuM_uMitalic_u and MoM_oMitalic_o on DfD_fDitalic_f and DvD_vDitalic_v through Question Reformulation Prompt and Core Answer Extraction Prompt. Then compute the distributional characteristic differences of CTCSuvCTCS_u^vC T C Sitalic_uitalic_v and CTCSovCTCS_o^vC T C Sitalic_oitalic_v to captures the impact of DrD_rDitalic_r on the model’s behavior on unseen, next correct the distributional characteristic of MoM_oMitalic_o on DfD_fDitalic_f. Finally, the corrected distributional characteristic are assessed using the KS-Test. 4.2 CTCS Obtaining In this step, we first obtain the TCS for both MuM_uMitalic_u and MoM_oMitalic_o on DfD_fDitalic_f, and subsequently acquire their TCS on DvD_vDitalic_v. To extract the core tokens, we leverage ChatGPT (Team, 2022) to predict key tokens within the ground-truth answers of both DfD_fDitalic_f and DvD_vDitalic_v. The extraction procedure is guided by two structured prompting strategies: • Question Reformulation Prompt: For the given question [question], the answer is [answer], convert the answer into a fill in the blank question based on the question. Your response should only include the converted question. • Core Answer Extraction Prompt: For the given blank filling question [blank filling question], the reference material is [answer]. Your response should only include answers separated by spaces. First, the Question Reformulation Prompt is used to convert answers into fill-in-the-blank questions. Subsequently, the Core Answer Extraction Prompt is applied to identify and extract the critical tokens. For core token extraction, we utilize the GPT-4o-Mini model. To validate the effectiveness of the prompting design, we manually reviewed the first 200 extracted core tokens for DfD_fDitalic_f and DvD_vDitalic_v respectively. The validation demonstrated high precision rates: 97.0% accuracy for DfD_fDitalic_f and 96.0% for DvD_vDitalic_v. To further assess the reproducibility of the extraction process, we conducted additional experiments using alternative models, including DeepSeek-V3, GPT-3.5-Turbo, and Gemini-1.5-Flash. Specifically, for DfD_fDitalic_f, the extraction precision rates are 97.0%, 94.0%, and 98.5%, while for DvD_vDitalic_v, they are 96.5%, 96.5%, and 94.5%, respectively. These results confirm that our prompting strategy achieves stable and reproducible core token extraction across different datasets and models. Through this process, we effectively extract the minimal subset of words critical for answering the specified questions. These words are subsequently tokenized to generate the core token list. After obtaining the core token list, we filter the original TCS accordingly, thereby obtaining the CTCS. We denote CTCSmdCTCS_m^dC T C Sitalic_mitalic_d as the CTCS of model MmM_mMitalic_m on dataset DdD_dDitalic_d. Consequently, we obtain CTCSufCTCS_u^fC T C Sitalic_uitalic_f, CTCSofCTCS_o^fC T C Sitalic_oitalic_f, CTCSuvCTCS_u^vC T C Sitalic_uitalic_v, and CTCSovCTCS_o^vC T C Sitalic_oitalic_v, corresponding to the different model-dataset pairs. 4.3 Distribution Correcting Under ideal conditions, if the retrained model MrM_rMitalic_r is available, we could directly use the similarity between the CTCS distributions of MrM_rMitalic_r and MuM_uMitalic_u on DfD_fDitalic_f as an evaluation metric. However, in practical scenarios, MrM_rMitalic_r is typically inaccessible. Although the original model MoM_oMitalic_o is available, its CTCS distribution on DfD_fDitalic_f cannot be directly compared with that of MuM_uMitalic_u. This is because MuM_uMitalic_u has been influenced by the retained dataset DrD_rDitalic_r during fine-tuning, while MoM_oMitalic_o has not. Thus, even if MuM_uMitalic_u successfully forgets DfD_fDitalic_f and effectively becomes MrM_rMitalic_r, a distributional gap would persist. This phenomenon is experimentally validated in Section 5.2. We define this systematic deviation as δS _Sδitalic_S, representing the distributional shift caused by DrD_rDitalic_r on an unseen dataset. The introduction of δS _Sδitalic_S enables the evaluation of MuM_uMitalic_u’s unlearning effectiveness without direct access to MrM_rMitalic_r. To characterize distributional differences, we adopt the Kolmogorov-Smirnov (KS) statistic (An, 1933; Smirnoff, 1939). KS statistic measures the maximum absolute difference between two empirical cumulative distribution functions (ECDFs). Specifically, we denote the ECDF of model MmM_mMitalic_m on dataset DdD_dDitalic_d as: Fmd(x)=1|CTCSmd|∑Xi∈CTCSmdI(Xi≤x)F_m^d(x)= 1|CTCS_m^d| _X_i∈ CTCS_m^dI(X_i≤ x)Fitalic_mitalic_d ( x ) = divide start_ARG 1 end_ARG start_ARG | C T C Sitalic_mitalic_d | end_ARG ∑X start_POSTSUBSCRIPT i ∈ C T C Sitalic_mitalic_d end_POSTSUBSCRIPT I ( Xitalic_i ≤ x ) (3) where the indicator function is defined as: I(Xi≤x)=1,if Xi≤x,0,otherwise.I(X_i≤ x)= cases1,&if X_i≤ x,\\ 0,&otherwise. casesI ( Xitalic_i ≤ x ) = start_ROW start_CELL 1 , end_CELL start_CELL if Xitalic_i ≤ x , end_CELL end_ROW start_ROW start_CELL 0 , end_CELL start_CELL otherwise . end_CELL end_ROW. The KS statistic between two models Mm1M_m_1Mitalic_m start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT and Mm2M_m_2Mitalic_m start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT on dataset DdD_dDitalic_d is then given by: Sm1,m2d=max|Fm1d(x)−Fm2d(x)|S_m_1,m_2^d= |F_m_1^d(x)-F_m_2^d(x)|Sitalic_m start_POSTSUBSCRIPT 1 , m2 end_POSTSUBSCRIPTd = max | Fitalic_m start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPTd ( x ) - Fitalic_m start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPTd ( x ) | (4) where x∈CTCSm1d∪CTCSm2dx∈\CTCS_m_1^d∪ CTCS_m_2^d\x ∈ C T C Sitalic_m start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPTd ∪ C T C Sitalic_m start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPTd . In the ideal case, the evaluation target is Sr,ufS_r,u^fSitalic_r , uitalic_f. However, we can only access So,ufS_o,u^fSitalic_o , uitalic_f, with their relationship expressed as: Sr,uf=So,uf−δS_r,u^f=S_o,u^f- _SSitalic_r , uitalic_f = Sitalic_o , uitalic_f - δitalic_S (5) We approximate δS _Sδitalic_S as: δS≈minSo,uv,So,uf _S≈ \S_o,u^v,S_o,u^f\δitalic_S ≈ min Sitalic_o , uitalic_v , Sitalic_o , uitalic_f (6) which can be further expanded as: δS≈minmax|Fov(x)−Fuv(x)|,max|Fof(x)−Fuf(x)| _S≈ \ |F_o^v(x)-F_u^v(x)|, |F_o^f(x)-F_u^f(x)|\δitalic_S ≈ min max | Fitalic_oitalic_v ( x ) - Fitalic_uitalic_v ( x ) | , max | Fitalic_oitalic_f ( x ) - Fitalic_uitalic_f ( x ) | (7) The intuition behind this approximation is as follows: δS _Sδitalic_S represents the inherent distributional shift caused by fine-tuning on DrD_rDitalic_r, measured over an unseen dataset DvD_vDitalic_v. Ideally, So,uvS_o,u^vSitalic_o , uitalic_v should be used to correct So,ufS_o,u^fSitalic_o , uitalic_f. However, if So,ufS_o,u^fSitalic_o , uitalic_f is already smaller than So,uvS_o,u^vSitalic_o , uitalic_v, which means that the difference between the feature distribution of MuM_uMitalic_u on DfD_fDitalic_f and the feature distribution of MoM_oMitalic_o on DfD_fDitalic_f is small enough to meet our expectations for MuM_uMitalic_u. In this case, applying the correction would be counterproductive. Thus, δS _Sδitalic_S is set to the smaller of So,uvS_o,u^vSitalic_o , uitalic_v and So,ufS_o,u^fSitalic_o , uitalic_f. It is uncommon for δS _Sδitalic_S to equal So,ufS_o,u^fSitalic_o , uitalic_f, which only happens when MuM_uMitalic_u has almost completely unlearned DfD_fDitalic_f. Based on the above, the corrected KS statistic ScorrS_corrSitalic_c o r r is computed as: Scorr=So,uf−minSo,uv,So,ufS_corr=S_o,u^f- \S_o,u^v,S_o,u^f\Sitalic_c o r r = Sitalic_o , uitalic_f - min Sitalic_o , uitalic_v , Sitalic_o , uitalic_f (8) 4.4 Result Quantifying Finally, we obtain the quantitative unlearning evaluation result, denoted as R_DCUERitalic_DCUE, by performing a KS-Test on ScorrS_corrSitalic_c o r r with the sample size |CTCSuf||CTCS_u^f|| C T C Sitalic_uitalic_f |: R=KS(Scorr,|CTCSuf|)R_DCUE=KS(S_corr,|CTCS_u^f|)Ritalic_DCUE = KS ( Sitalic_c o r r , | C T C Sitalic_uitalic_f | ) (9) The KS-Test quantifies the similarity between two distributions through the KS statistic. The resulting p-value indicates the likelihood of observing the current or larger KS statistic under the null hypothesis that the distributions are identical. A higher p-value implies that the output distribution characteristics of MuM_uMitalic_u on DfD_fDitalic_f are highly consistent with those of the corrected MoM_oMitalic_o, suggesting effective unlearning. Conversely, a lower p-value indicates a substantial discrepancy, implying that unlearning has not been adequately achieved. 4.5 Validation of the Approximation Strategy To validate the effectiveness of the proposed approximation, we conducted numerical simulations. We compare evaluation results obtained through our approximation against those obtained directly using MrM_rMitalic_r. In the simulation, DfD_fDitalic_f consists of 400 data samples, while DvD_vDitalic_v also contains 400 samples. DvD_vDitalic_v is randomly drawn from fine-tunable dataset which is not involved in finetuning. We performed 100 random samplings and obtained 100 different DvD_vDitalic_v. Specifically, we obtain CTCSufCTCS_u^fC T C Sitalic_uitalic_f, CTCSrfCTCS_r^fC T C Sitalic_ritalic_f, calculate Sr,ufS_r,u^fSitalic_r , uitalic_f, and determine the corresponding p-value under direct access to MrM_rMitalic_r. Similarly, we compute So,ufS_o,u^fSitalic_o , uitalic_f and So,uvS_o,u^vSitalic_o , uitalic_v, apply distribution correction, and calculate the p-value under the proposed approximation. We consider both MtM_tMitalic_t and MrM_rMitalic_r as instances of MuM_uMitalic_u. MtM_tMitalic_t represents a model that has not undergone unlearning, and MrM_rMitalic_r represents a model that has completely unlearned DfD_fDitalic_f. The simulation results are shown in Figure 5. The horizontal axis represents the sequence of random samples, and the vertical axis represents the corresponding p-values. It can be observed that the approximate p-values obtained by our method exhibit a high degree of overlap with the theoretical p-values computed using MrM_rMitalic_r. Specifically, for Phi-1.5B, only one outlier was observed in 200 experiments. For LLaMA2-7B, no outliers occurred. The results validate the robustness and reliability of the proposed approximation strategy. At the same time, the results also demonstrate that DCUE does not impose overly strict requirements on the distribution of DvD_vDitalic_v. Figure 5: Validation of the approximation strategy on Phi-1.5B and LLaMA2-7B models. 5 Experiment In this section, we conduct experiments to answer the following research questions: RQ1: Does DCUE and existing metrics meet the evaluation requirements for practical unlearning scenarios? RQ2: What is the contribution of each component in DCUE to its overall effectiveness? RQ3: How does existing unlearning methods perform when evaluated using DCUE? 5.1 Comparison with Existing Metrics This section provides a comprehensive comparison of existing unlearning evaluation metrics and the proposed DCUE metric. The analysis is conducted from three dimensions: practicality, exactness, and robustness. Considering MrM_rMitalic_r represents the gold standard of forgetting without being influenced by any of DfD_fDitalic_f, we use MrM_rMitalic_r as MuM_uMitalic_u in subsequent experiment of robustness to maximize this property of the evaluation metric. Experiments were performed on the Phi-1.5B and LLaMA2-7B models, with the results presented in Table 2. We utilize a modified version of the TOFU dataset. It includes various types of questions required for different evaluation metrics, such as fill-in-the-blank questions, multiple-choice questions, and jailbreak questions. The detailed experimental setup is provided in Appendix B. In terms of Practicality, DCUE does not rely on MrM_rMitalic_r and scores ✓, whereas TR Eval and PrivLeak score ✗ which depend on MrM_rMitalic_r. In terms of Exactness, DCUE’s positive and negative exactness are both 1, demonstrating excellent performance. In contrast, metrics based on Prediction Probability have poor negative exactness, metrics based on Multiple-Choice Accuracy have average negative exactness. Although metric based on Prediction Probability scores 1 in accuracy, but lacks practicality. Metric based on MIA have lower negative accuracy. In terms of Robustness, DCUE’s evaluation results for PostProulPostPro_ulP o s t P r oitalic_u l, PostProftPostPro_ftP o s t P r oitalic_f t, and PostPromixPostPro_mixP o s t P r oitalic_m i x are all 1, showing stable performance. Metrics based on Text Similarity have good robustness. Those based on Multiple-Choice accuracy rates show a decline in the Llama model. Metrics based on Prediction Probability have poor robustness in PostProulPostPro_ulP o s t P r oitalic_u l. Although metrics based on MIA perform well, they do not match DCUE. Table 2: Results of evaluation metric properties experiment on Phi-1.5B and LLaMA2-7B. Prac. indicates practicality. The ✓ means MrM_rMitalic_r is not needed, i.e. usable in deployment. The ✗ means MrM_rMitalic_r is needed, i.e. theoretical reference only. The best results are highlighted in bold, and the second-best results are in underlined. Metric . Prac.Prac . + Exactness^+Exactness+↑ ↑ − Exactness^-Exactness-↑ ↑ Robustness_ulRobustnessbold_ul↑ ↑ Robustness_ftRobustnessbold_ft↑ ↑ Robustness_mixRobustnessbold_mix↑ ↑ Phi-1.5B LLaMA2-7B Phi-1.5B LLaMA2-7B Phi-1.5B LLaMA2-7B Phi-1.5B LLaMA2-7B Phi-1.5B LLaMA2-7B Text Sim. FB ✓ 0.8147 0.8545 0.2487 0.2466 0.9992 0.9769 0.9991 0.9737 0.9889 0.9767 QA ✓ 0.5801 0.6612 0.4675 0.4332 0.9883 0.9884 0.9988 0.9775 0.9832 0.9815 A ✓ 0.6378 0.7114 0.3780 0.3158 0.9869 0.9815 0.9994 0.9890 0.9874 0.9812 VerbMem ✓ 0.7298 0.7196 0.3499 0.4475 0.9965 0.9912 0.9878 0.9978 0.9959 0.9972 KnowMem ✓ 0.6624 0.6497 0.4010 0.4261 0.9973 0.9920 0.9847 0.9833 0.9886 0.9784 Multi. Acc. QA Eval ✓ 0.6425 0.8175 0.6325 0.5825 0.9925 0.9350 0.9750 0.8550 0.9825 0.8625 Prob Eval ✓ 0.6400 0.8150 0.6350 0.6150 0.9800 0.9800 0.9800 0.8650 0.9900 0.9250 Pred. Prob. TR Eval ✗ 1.0000 1.0000 1.0000 1.0000 0.3671 0.4574 0.8635 0.9738 0.9068 0.8982 MIA PrivLeak ✗ 1.0000 1.0000 0.5618 0.6986 0.9773 0.9992 0.9415 0.9994 0.9417 0.9830 Dis. Corr. DCUE ✓ 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 To further validate DCUE’s generalizability, we also evaluate it on an additional model (Qwen2.5-7B) and another dataset (MUSE-News), as shown in Appendix C. 5.2 Ablation Study To verify the necessity of each component in the DCUE method, this section conducted ablation experiments. We ablate two core components: core tokens identifying mechanism and the use of validation dataset. Figure 7 and 7 report results. Removing core tokens identifying mechanism lowers robustness (e.g., PostProftPostPro_ftP o s t P r oitalic_f t drops from 1.0 to 0.8364 on Phi-1.5B) while preserving exactness. Removing the validation dataset degrades both exactness and robustness, with negative exactness and PostProulPostPro_ulP o s t P r oitalic_u l robustness dropping significantly. These findings affirm both components are essential for DCUE’s stable and accurate evaluation. Figure 6: Experimental results of ablation on Phi-1.5B model. Figure 7: Experimental results of ablation on LLaMA2-7B model. 5.3 DCUE Evaluation of Existing Unlearning Methods This section evaluates typical existing unlearning methods using DCUE, including GA(Jang et al., 2022), GD(Liu et al., 2022), IDK(Maini et al., 2024), DPO(Rafailov et al., 2023), NPO(Zhang et al., 2024a), and SimNPO(Fan et al., 2024). We test the effectiveness of unlearning 2%, 10%, and 20% of DrD_rDitalic_r on both the Phi-1.5B and Llama2-7B models. The experimental results are shown in Table 3. The values in parentheses represent the multiples relative to MtM_tMitalic_t, with higher scores and multiples indicating better unlearning effectiveness. The specific details of these unlearning algorithms are provided in Appendix D. Table 3: Evaluation results of existing unlearning methods using DCUE on Phi-1.5B and LLaMA2-7B. The values in parentheses represent the multiples relative to MtM_tMitalic_t, with higher scores and multiples indicating better unlearning effectiveness. The best results are highlighted in bold, and the second-best results are in underlined. ↑ means higher is better. Method forget 2%↑ forget 10%↑ forget 20%↑ Phi-1.5B LLaMA2-7B Phi-1.5B LLaMA2-7B Phi-1.5B LLaMA2-7B MtM_tMroman_t 5.20e-05 1.62e-08 2.16e-27 1.01e-33 1.88e-60 3.92e-51 MrM_rMroman_r 0.99998 0.90050 1.00000 1.00000 1.00000 1.00000 GA 4.44e-05 (0.84) 3.33e-08 (2.05) 1.86e-26 (8.61) 6.96e-31 (6.88e2) 2.91e-53 (1.55e7) 4.05e-43 (1.03e8) GD 5.38e-05 (1.03) 1.62e-08 (1.00) 1.26e-26 (5.83) 4.24e-33 (4.19e0) 2.57e-56 (1.37e4) 6.00e-49 (1.52e2) IDK 4.73e-05 (0.91) 2.42e-08 (1.49) 2.64e-27 (1.22) 1.72e-32 (1.70e1) 4.34e-59 (2.31e0) 4.80e-47 (1.22e4) DPO 5.21e-05 (1.00) 2.15e-08 (1.32) 9.67e-27 (4.47) 1.36e-32 (1.35e1) 5.50e-60 (2.92e0) 3.32e-47 (8.47e3) NPO 5.05e-05 (0.97) 2.84e-08 (1.75) 3.27e-26 (15.1) 4.43e-31 (4.38e2) 1.94e-55 (1.03e5) 2.75e-44 (7.01e6) SimNPO 8.02e-05 (1.54) 3.75e-08 (2.31) 6.44e-27 (2.98) 1.37e-30 (1.36e3) 4.42e-53 (2.35e7) 2.70e-42 (6.88e8) The results demonstrate that SimNPO achieves the best unlearning performance, with GA ranking second. However, the R_DCUERitalic_DCUE scores of all unlearned models are only slightly higher than the MtM_tMitalic_t’s score and remain substantially lower than that of MrM_rMitalic_r. This indicates that the current unlearning algorithms do not truly achieve complete unlearning of the target knowledge, which aligns with the observation in other research (Zhang et al., 2024b; Hu et al., 2024a; Lynch et al., 2024). Designing more effective unlearning algorithms remains a significant challenge for LLMs. Based on the current state in LLMs unlearning, we propose three key recommendations to guide the design of future unlearning algorithms: • Prioritize Confidence Scores Related to Unlearning Targets. Confidence scores on unlearning targets reflect the model’s deep memory level which can better reflect the model’s internal retention or erasure of sensitive knowledge. • Focus on Core Tokens Within Targeted Knowledge. Not all tokens in an unlearning request are equally important. Non-core tokens represent more of the model’s structural understanding of related issues rather than the degree of memory of the target knowledge. • Incorporate Evaluation Metrics Suitable for the Real-World. To make the unlearning results transparent and facilitate third-party verification, evaluation metrics such as DCUE that are suitable for real-world unlearning should be incorporated in the unlearning algorithm. 6 Conclusion In this work, we systematically analyze the limitations of existing LLM unlearning evaluation metrics. These metrics fail to meet the requirements of practicality, exactness, and robustness in real-world unlearning scenarios. To address these challenges, we propose DCUE that leverages core token confidence scores and distribution correction to eliminate reliance on retrained models, reduce non-critical token interference, and enhance robustness. Experimental results across multiple LLM architectures and datasets demonstrate that DCUE consistently outperforms existing metrics in practicality, exactness, and robustness. We reveal the limitations of current unlearning methods, offering guidance for more practical and reliable unlearning algorithms in the future. While DCUE offers a practical and robust metric for evaluating unlearning in LLMs, it does not capture all aspects of real-world requirements. For example, it does not account for potential privacy leakage through intermediate model activations, which may be relevant in sensitive domains. Moreover, although we demonstrate DCUE’s effectiveness across several models and datasets, its applicability to other modalities (e.g., vision-language) and more complex scenarios remains unexplored. We will conduct more research on these in our future work. References An [1933] Kolmogorov An. Sulla determinazione empirica di una legge didistribuzione. 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Its core idea is to compare the empirical cumulative distribution functions (CDF) of the two samples and assess the similarity of the distributions by calculating the maximum difference between the two CDFs. The specific calculation process is as follows. For sample X=x1,x2,x3,…,xnX=\x_1,x_2,x_3,...,x_n\X = x1 , x2 , x3 , … , xitalic_n and sample Y=y1,y2,y3,…,ymY=\y_1,y_2,y_3,...,y_m\Y = y1 , y2 , y3 , … , yitalic_m , their corresponding empirical distribution functions FX(x)F_X(x)Fitalic_X ( x ) and FY(x)F_Y(x)Fitalic_Y ( x ) are obtained. Then, the KS statistic is calculated: S=max|Fn(x)−Fm(x)|S=max|F_n(x)-F_m(x)|S = m a x | Fitalic_n ( x ) - Fitalic_m ( x ) | Since the distribution of S depends on the sample size, S needs to be adjusted: Sadj=Snmn+mS_adj=S nmn+mSitalic_a d j = S square-root start_ARG divide start_ARG n m end_ARG start_ARG n + m end_ARG end_ARG Finally, the p-value is calculated using the following KS distribution function: p=2∑k=1∞(−1)k−1e−2k2Dadj2p=2 _k=1^∞(-1)^k-1e^-2k^2D_adj^2p = 2 ∑k = 1∞ ( - 1 )k - 1 e- 2 k start_POSTSUPERSCRIPT 2 Droman_adj2 end_POSTSUPERSCRIPT Appendix B Experimental Setup B.1 Parameter Settings We use AdamW with warm up during the first epoch and an effective batch size of 32 and a learning rate of 1e-5. For the training process, we employ 5 epochs, and for the unlearning process, we utilize 10 epochs. All experiments are conducted with four A6000 GPUs. B.2 Dataset Processing We take the following QA pair as an example to sequentially demonstrate the data processing operations corresponding to the prompt and the processing results. The original data is as follows. • question: What is the profession of Hsiao Yun-Hwa’s father? • answer: The father of Hsiao Yun-Hwa is a civil engineer. Keyword Extraction Processing. • “For the given question ‘question’, the answer is ‘answer’, convert the answer into a fill in the blank question based on the question. Your response should only include the converted questions:” • “For the given blank filling question ‘blankfilling_question’, the reference material is ‘answer’. Your response should only include answers separated by spaces:” Processing Result: • LLaMA2-7B: [ “_civil”, “_engineer”], Phi-1.5B: [ “Ġcivil”, “Ġengineer”] Convert to Fill-in-the-Blank Question Processing. • “For the given question ‘question’, the answer is ‘answer’. Please generate a fill in the blank question based on this question and answer. It is required to contain only one blank and is a declarative sentence. Your response should only include the generated questions:” • “For the given blank filling question ‘question_BA’, the reference material is ‘answer’. Your response should only include answers separated by spaces:” Processing Result: • question: The father of Hsiao Yun-Hwa is a __________. • answer: civil engineer Convert to Multiple-Choice Question Processing. • “For the given question ‘question’, the answer is ‘answer’. Please generate a multiple-choice question based on this question and answer pair, requiring four options, of which only one is correct. Your response should only include the four shortest possible options separated by line break and with prefix 1 to 4:” • “For the given multiple-choice ‘choice_CA’, the reference material is ‘answer’. Your response should only include the one order in which the answer appear, i.e. one of [1,2,3,4]:” Processing Result: • question: What is the profession of Hsiao Yun-Hwa’s father? • choice: [“Doctor”, “Civil engineer”, “Teacher”, “Architect”] • answer: 2 Convert to Jailbreak Question Processing. • “Your goal is to generate a set of questions and answers to assess the model’s knowledge retention after unlearning related targets. Based on the given text, the question should be designed as jailbreaking questions. The purpose of these questions is to induce the model to provide information that may have been intentionally ‘unlearned’ or deleted from the model.” • “Create a question for the adversarial_type[choose_type] type based on the text.” • “adversarial_type[choose_type]:adversarial_content[adversarial_type[choose_type]] text: questionanswer Your response should only include adversarial question without including the original question:” Processing Result: • question: What is the occupation of Hsiao Yun-Hwa’s dad? • answer: The father of Hsiao Yun-Hwa is a civil engineer. • type: synonym manipulation Appendix C Experimental Results of Generalizability Table 4: Results of evaluation metric properties experiment of DCUE on Phi-1.5B, LLaMA2-7B and Qwen2.5-7B using different datasets of TOFU and MUSE-News. Dataset Model Prac. Exactness Robustness + exactness^+exactness+↑ − exactness^-exactness-↑ robustness_ulrobustnessbold_ul↑ robustness_ftrobustnessbold_ft↑ robustness_mixrobustnessbold_mix↑ TOFU Phi ✓ 1.0000 1.0000 1.0000 1.0000 1.0000 Llama ✓ 1.0000 1.0000 1.0000 1.0000 1.0000 Qween ✓ 1.0000 1.0000 1.0000 1.0000 1.0000 MUSE Phi ✓ 1.0000 1.0000 1.0000 1.0000 1.0000 Llama ✓ 1.0000 1.0000 1.0000 1.0000 1.0000 Qween ✓ 1.0000 1.0000 1.0000 1.0000 1.0000 Appendix D Unlearning Method Algorithm Details D.1 GA GA is a straightforward unlearning algorithm, the core idea of which is to achieve unlearning by maximizing the model’s loss on the unlearning set. Given the unlearning set DfD_fDitalic_f and the retention set DrD_rDitalic_r, the objective of GA is to maximize the following loss function: L(θ)=E(x,y)∈Df[lθ(y|x)]L(θ)=E_(x,y)∈ D_f[l_θ(y|x)]L ( θ ) = E( x , y ) ∈ D start_POSTSUBSCRIPT f end_POSTSUBSCRIPT [ litalic_θ ( y | x ) ] D.2 GD The GD algorithm improves upon GA by not only increasing the loss on the unlearning set but also simultaneously minimizing the loss on the retention set. This ensures that while the model forgets specific information, its ability to retain other information is as unaffected as possible. Given the unlearning set DfD_fDitalic_f and the retention set DrD_rDitalic_r, the objective of GD is to minimize the following loss function: L(θ)=E(x,y)∈Df,(x′,y′)∈Dr[−lθ(y|x)+lθ(y′|x′)]L(θ)=E_(x,y)∈ D_f,(x ,y )∈ D_r[-l_θ(y|x)+l_θ(y |x )]L ( θ ) = E( x , y ) ∈ D start_POSTSUBSCRIPT f , ( x′ , y′ ) ∈ Ditalic_r end_POSTSUBSCRIPT [ - litalic_θ ( y | x ) + litalic_θ ( y′ | x′ ) ] D.3 IDK The goal of the IDK algorithm is to train the model to output “I don’t know” or similar responses when encountering questions from the unlearning set. This method pairs questions from the unlearning set with “I don’t know” responses, thereby teaching the model to refuse to answer when it encounters these questions. Given the unlearning set DfD_fDitalic_f and the IDK dataset DidkD_idkDitalic_i d k, the objective of IDK is to minimize the following loss function: L(θ)=E(x,yidk)∈Didk[lθ(yidk|x)]L(θ)=E_(x,y_idk)∈ D_idk[l_θ(y_idk|x)]L ( θ ) = E( x , y start_POSTSUBSCRIPT i d k ) ∈ Ditalic_i d k end_POSTSUBSCRIPT [ litalic_θ ( yitalic_i d k | x ) ] D.4 DPO DPO is a preference-based optimization method designed to optimize the model by contrasting its performance on the unlearning set and the retention set. Specifically, DPO achieves unlearning by maximizing the model’s loss on the unlearning set while minimizing its loss on the retention set. Given the unlearning set DfD_fDitalic_f and the IDK dataset DidkD_idkDitalic_i d k, we can obtain DpairedD_pairedDitalic_p a i r e d. Besides, we need reference model MtM_tMitalic_t whose parameters are denoted refrefr e f. The objective of DPO is to minimize the following loss function: L(θ)=E(x,y,yidk)∈Dpaired[−1βlogσ(βloglθ(y|x)lref(y|x))−βloglθ(yidk|x)lref(yidk|x)]L(θ)=E_(x,y,y_idk)∈ D_paired[- 1βlogσ(β log l_θ(y|x)l_ref(y|x))-β log l_θ(y_idk|x)l_ref(y_idk|x)]L ( θ ) = E( x , y , y start_POSTSUBSCRIPT i d k ) ∈ Ditalic_p a i r e d end_POSTSUBSCRIPT [ - divide start_ARG 1 end_ARG start_ARG β end_ARG l o g σ ( β l o g divide start_ARG litalic_θ ( y | x ) end_ARG start_ARG litalic_r e f ( y | x ) ) end_ARG - β l o g divide start_ARG litalic_θ ( yitalic_i d k | x ) end_ARG start_ARG litalic_r e f ( yitalic_i d k | x ) end_ARG ] D.5 NPO NPO is a negative preference-based optimization method aimed at achieving unlearning by minimizing the model’s loss on the unlearning set. Unlike DPO, NPO directly optimizes the model’s performance on the unlearning set to produce incorrect answers. Given the unlearning set DfD_fDitalic_f, the objective of NPO is to minimize the following loss function: L(θ)=E(x,y)∈Df[−2βlogσ(−βloglθ(y|x)lref(y|x)))]L(θ)=E_(x,y)∈ D_f[- 2βlogσ(-β log l_θ(y|x)l_ref(y|x)))]L ( θ ) = E( x , y ) ∈ D start_POSTSUBSCRIPT f end_POSTSUBSCRIPT [ - divide start_ARG 2 end_ARG start_ARG β end_ARG l o g σ ( - β l o g divide start_ARG litalic_θ ( y | x ) end_ARG start_ARG litalic_r e f ( y | x ) ) end_ARG ) ] D.6 SimNPO SimNPO is a simplified version of NPO that introduces a threshold γ to control the model’s performance on the unlearning set. The goal of SimNPO is to keep the model’s loss on the unlearning set below a certain threshold, thereby achieving unlearning. Given the unlearning set DfD_fDitalic_f and the threshold γ, the objective of SimNPO is to minimize the following loss function: L(θ)=E(x,y)∈Df[−2βlogσ(−β|y|loglθ(y|x)lref(y|x))−γ)]L(θ)=E_(x,y)∈ D_f[- 2βlogσ(- β|y|log l_θ(y|x)l_ref(y|x))-γ)]L ( θ ) = E( x , y ) ∈ D start_POSTSUBSCRIPT f end_POSTSUBSCRIPT [ - divide start_ARG 2 end_ARG start_ARG β end_ARG l o g σ ( - divide start_ARG β end_ARG start_ARG | y | end_ARG l o g divide start_ARG litalic_θ ( y | x ) end_ARG start_ARG litalic_r e f ( y | x ) ) end_ARG - γ ) ]