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REVIEW 3 major objections 8 minor 1 cited by

Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?

T0 review · 3 major / 8 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that LLM unlearning is a special case of knowledge editing—replace the target answer with a refusal—and shows that WISE and AlphaEdit are strong unlearning baselines, especially for pretrained knowledge.

desk verdict Useful bridge paper, but the 'unlearning' it benchmarks is behavioral refusal, not knowledge erasure, so the strong-baseline claim needs that caveat. read the letter →

arxiv 2505.19855 v1 pith:LF6ZWSGN submitted 2025-05-26 cs.LG

Zexi Li , Xiangzhu Wang , William F. Shen , Meghdad Kurmanji , Xinchi Qiu , Dongqi Cai , Chao Wu , Nicholas D. Lane This is my paper · ORCID
classification cs.LG
keywords LLMunlearningknowledgeeditingasrefusalanswersWISEAlphaEdithumanvaluealignmentquerymerging
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Unlearning, the paper argues, is not a separate task from knowledge editing: it is the special case where the edited answer is a refusal, the 'empty set' of model knowledge. The authors rewrite the editing objective so that forget queries map to a refusal answer ($\emptyset$) while everything else is preserved, which converts any editing method into an unlearning method. On TOFU's factual set and PISTOL, they find that WISE and AlphaEdit—two lifelong editing methods—are strong unlearning baselines, especially for pretrained knowledge, and that their refusal outputs are rated more human-aligned than those of gradient-based unlearning. Two recipes, self-improvement and query merging, adapt editing methods further, and the paper urges the unlearning community to adopt editing baselines and treat refusal quality as an evaluation axis.

What carries the argument

The load-bearing mechanism is Equation 3, the editing-as-unlearning identity: replace each forget-query answer with a common refusal target $\emptyset$ and run an editing algorithm, so the model learns the mapping query $\to$ refusal while preserving other outputs. The empirical engine is the pair of lifelong editing methods WISE, which stores edits in a dynamic parametric side memory, and AlphaEdit, which constrains parameter updates to the null space to avoid side effects; they are supported by two adaptations: self-improvement, which uses the model's own in-context learning to craft tailored, entity-aware refusal answers, and query merging, which concatenates several forget queries into one edit with a single refusal target so that locate-and-edit methods like ROME and MEMIT can handle long unlearning sequences.

What would settle it

Run the paper's setting with WISE and AlphaEdit on a factual forget set, then probe the unlearned model's hidden activations on the original query (for example, train a linear probe on the model's own hidden states to decode the target answer, or measure the conditional probability of the correct answer when the refusal is bypassed by a paraphrased or 'continue' prompt). If the correct fact can still be decoded or elicited at high accuracy, the behavioral equivalence between refusal and erasure—and with it the claim that editing methods are unlearning methods—collapses.

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Extended reading notes

Core claim

The paper's central claim is that knowledge editing and LLM unlearning are not separate tasks: unlearning is the special case of editing in which the target answer is replaced by a refusal, the 'empty set' $\emptyset$ of generative answers. Writing the editing objective as $f_{\Theta^*}(x)=\emptyset$ for forget queries and $f_{\Theta_0}(x)$ otherwise (Equation 3) turns any editing method into an unlearning method. On that basis the paper evaluates five editing methods (ROME, MEMIT, GRACE, WISE, AlphaEdit) against four unlearning baselines (GA, GD, KL, DPO) on TOFU's factual set (pretrained knowledge) and PISTOL (finetuned knowledge). It reports that WISE and AlphaEdit are the strongest editing methods and are competitive or superior to unlearning baselines, especially on pretrained knowledge, and that they produce refusal answers that human raters find more trustworthy and semantically entailed. The paper also claims that its self-improvement pipeline and query merging technique extend editing methods' reach, making even ROME and MEMIT perform well on long unlearning sequences.

Load-bearing premise

The assumption that carries the argument is that a model has unlearned when it refuses to answer a forget query, measured only on outputs; the paper never checks whether the underlying fact was actually removed from the model's parameters.

Editorial extensions

If this is right

  • Unlearning benchmarks should list WISE and AlphaEdit as standard baselines, since the paper shows they hold the forget/retain trade-off on pretrained factual knowledge.
  • Refusal quality should become a reported unlearning metric: pretrained-knowledge experiments in the paper show editing methods produce refusal answers rated higher on trustworthiness and semantic entailment than gradient-based unlearning.
  • The self-improvement pipeline implies that the editing target for unlearning can be synthesized by the model itself, improving rephrase-attack generalization at the cost of an alignment tax for WISE.
  • Query merging shows that ROME and MEMIT, previously ineffective under long sequences, can become competitive unlearning methods when several forget queries share one refusal target.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The authors' behavioral definition of forgetting—refusal at the output—does not establish that the fact is gone from the weights; a natural next test is probing hidden states of an 'unlearned' model to see whether the correct answer can still be decoded.
  • The refusal-as-$\emptyset$ equivalence suggests unlearning evaluation will eventually need to separate 'refusal to say' from 'genuine absence of knowledge,' because privacy and safety goals differ: a model that can be prompted or jailbroken back to the fact has not been unlearned in a legal sense.
  • Because many forget queries naturally share one refusal target, query merging could be generalized to group-level or topic-level unlearning, with the expected trade-off that longer merged contexts strain the underlying editing method.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 8 minor

Summary. This paper proposes to view LLM unlearning as a special case of knowledge editing in which the target answer is replaced by a refusal or 'empty set' response (Eq. 3). The authors evaluate five knowledge-editing methods (ROME, MEMIT, GRACE, WISE, AlphaEdit) against four unlearning methods (GA, GD, KL, DPO) on two datasets (TOFU factual/world knowledge for pretrained knowledge; PISTOL for finetuned knowledge) and three base models (Llama2-7B, Mistral-7B, Llama3.1-8B). They report that WISE and AlphaEdit are particularly strong unlearning baselines for pretrained knowledge, that editing methods produce more human-aligned refusal answers, and they propose two practical recipes: a self-improvement pipeline that uses the LLM itself to generate tailored refusal targets, and a query-merging technique that enables ROME and MEMIT to handle long forget sequences. The paper advocates that the unlearning community adopt state-of-the-art editing methods as baselines.

Significance. If the central claim is accepted, the paper offers a low-cost bridge between the knowledge-editing and unlearning communities: editing methods are mature and lightweight and could be plugged into unlearning benchmarks with minimal adaptation. The paper is a useful empirical contribution in that it compares a broader set of editing methods than previous work, evaluates on both pretrained and finetuned knowledge, and provides two concrete, reproducible recipes. The inclusion of human evaluation of refusal quality is a strength. However, the significance is limited by two factors: the evaluation measures behavioral refusal on the original query distribution rather than parametric erasure, and the paper's own rephrase-attack results show that the edited models still recall a substantial fraction of the target facts under rephrasing. As a result, the paper's headline recommendation that editing methods should be adopted as unlearning baselines is currently supported only under a narrow definition of unlearning; the significance would be substantially strengthened by evidence on knowledge retention or by an explicit reframing of the claim.

major comments (3)
  1. [3.1 and 4.1 (Eq. 3; Table 2)] The paper operationalizes unlearning purely behaviorally: Eq. (3) maps each forget query to a fixed refusal output, and the evaluation in Section 4.1 measures only output-surface metrics (Rouge1, Prob, MRR, Hit-Rate) on the original queries. No experiment checks whether the parametric knowledge is actually removed, despite the abstract's claim of "removing information from LLMs." The paper's own rephrase-attack results in Table 2 show residual recall: on the Llama2-7B factual dataset, WISE's forget-set Rouge1 increases from 0.28 on the original queries to 0.46 under GPT-4 rephrasings, and AlphaEdit's from 0.08 to 0.14. This indicates that the target facts remain retrievable when the query is rephrased and that the editing methods have learned to refuse on the training query distribution rather than to erase the underlying knowledge. This is structurally expected because WISE stores edits in side memory without modifying base parameters and AlphaEdit constrains updates to the null space to preserve existing knowledge. Since the central recommendation is that unlearning benchmarks should adopt editing methods as baselines, this recommendation currently rewards answer suppression on a narrow query distribution. Please either (i) explicitly reframe the claim as "strong baselines for producing refusal answers on the forget query distribution" and add a limitations paragraph discussing the distinction between behavioral refusal and parametric erasure, or (ii) add experiments that directly probe knowledge retention (e.g., diverse paraphrase attacks, internal representation probes, or counterfactual variants) and report those results.
  2. [Table 1; Appendix B.4] The comparative ranking that supports the central claim (e.g., Ob2 and Ob4) is reported without error bars, multiple seeds, or significance tests. Every cell in Table 1 and every point in Figures 3, 4, and 6 is a single run, so the reader cannot assess whether the differences between WISE/AlphaEdit and the unlearning baselines are meaningful. In addition, the hyperparameters for the editing methods are hand-tuned: Appendix B.4 states that mom2_n_samples for MEMIT and AlphaEdit is reduced to 1000 "considering computational resource constraints," while the unlearning baselines use learning rates taken from PISTOL. These choices are asymmetric and could affect the ranking. Please provide variance estimates (e.g., multiple seeds or random forget-set subsets) and justify the hyperparameter settings, or temper the comparative claims accordingly.
  3. [Table 3] The Llama3.1-8B results in Table 3 are mixed and are not discussed in the main text. On the factual dataset, WISE achieves the best forget Rouge1 (0.367) but the retain Rouge1 is only 0.592, well below GD (0.938) and AlphaEdit (0.847); AlphaEdit's forget Rouge1 is 0.517, which is worse than WISE's but still better than DPO's 0.45. The text's claim that "WISE and AlphaEdit consistently perform well across all settings" (Ob1) and that editing methods are strong for pretrained knowledge does not hold cleanly on this third model. Since the paper argues for a general recommendation to the unlearning community, please analyze these results explicitly and state the scope over which the claim is intended to apply.
minor comments (8)
  1. [3.1] The notation ∅ ("empty set") is used to denote a refusal sentence, which is mathematically a set rather than a string; please define a different symbol (e.g., r(x)) or clarify the abuse of notation.
  2. [Figure 1] In the provided text, the figure contains a garbled fragment ("archivihtml") and the text in panels is very small; please ensure the final figure is rendered legibly.
  3. [Appendix Table 4] In the WISE row, the value "0087" appears to be a typo for "0.087"; please fix.
  4. [4.4 / Figure 6 (left)] The human evaluation relies on only 20 participants and reports no inter-annotator agreement or significance tests; given the small differences (e.g., WISE trustworthiness 2.56 to 2.38, AlphaEdit entailment 1.55 to 1.73), please present these results as qualitative or add statistical support.
  5. [3.2 / Figure 6 (right)] Please specify how the merged queries are concatenated (e.g., separator tokens, instruction template) and how the single refusal target is generated, for reproducibility; the paper currently states only that several queries are concatenated into one.
  6. [4.2] The comparison is not fully controlled because GD and KL use the retain set while editing methods do not; the paper notes this at the end of Section 4.1, but the potential advantage to editing methods is not discussed. A brief discussion would help readers interpret the comparative rankings.
  7. [4.4 (Obs6)] The parenthetical "merging 5 queries into 1 (16 samples after merging)" is confusing; it would be clearer to write "80 forget samples, 80/5 = 16 editing samples."
  8. [Limitations] The Limitations section is very brief and does not mention the behavioral-versus-parametric distinction or the mixed Llama3.1 results; please expand it accordingly.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the evaluation is empirical and self-contained; self-citations to WISE and PISTOL are not load-bearing, and the unlearning-as-refusal framing is an operationalization rather than a result derived from itself.

full rationale

The paper's central claim is an empirical benchmark comparison, not a derivation. Equation 3 operationalizes unlearning as editing to a refusal target, but this is a stated modeling choice, and the conclusion that WISE and AlphaEdit are strong baselines rests on measurements in Tables 1-3, not on Eq. 3 alone. No parameter is fitted and then renamed a prediction; the self-improvement pipeline is evaluated by independent human raters, and query merging is an input transformation whose effects are measured. Self-citations to WISE and PISTOL are present, but the methods are executed and benchmarked rather than cited as authority for the conclusion, so they are not load-bearing. The definitional choice to treat refusal as empty-set knowledge is contestable, and the paper does not demonstrate parametric erasure; however, that is a correctness and scope concern, not a circular reduction. The paper therefore has no circular step that reduces its result to its inputs.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper's central claim does not rest on a mathematical derivation; it rests on empirical comparisons. The main hidden costs are hand-chosen hyperparameters (Appendix B.4), selection of the merged-query count on the evaluation set (Figure 6), and benchmark assumptions that TOFU factual QA pairs represent pretrained knowledge and that refusal behavior is equivalent to unlearning. No new physical or architectural entities are introduced.

free parameters (3)
  • learning rates for unlearning baselines = 2e-5 for GA/GD/KL, 1.5e-5 for DPO
    Taken from PISTOL and set by hand; the comparison between editing and unlearning methods depends on these hyperparameters (Appendix B.4).
  • mom2_n_samples for MEMIT and AlphaEdit = 1000 (reduced from default; default for ROME/GRACE/WISE)
    The covariance sample count is reduced for computational reasons, which can affect the stability and performance of MEMIT and AlphaEdit weight updates (Appendix B.4).
  • merged query count in query merging = 5 (swept 2-10; best on the same forget set)
    The number of merged queries is swept and the peak (16 merged samples for 80 forget samples) is highlighted, which is selection on the evaluation set (Section 4.4, Figure 6).
assumptions (3)
  • domain assumption The TOFU factual QA pairs are treated as knowledge the model acquired during pretraining.
    The paper calls this dataset 'pretrained knowledge' based on high pre-unlearning Rouge1 scores (0.82 for Llama2-7B, 0.86 for Mistral-7B), but actual membership in the pretraining corpus is not verified (Section 4.1, 4.2).
  • domain assumption Behavioral refusal to answer is an acceptable operationalization of unlearning.
    Equation (3) replaces the forgotten answer with an empty-set refusal and evaluates only model outputs; the paper does not test whether the parametric knowledge itself was erased, and related work cited in the paper notes that unlearning often obscures rather than removes data.
  • ad hoc to paper A single refusal answer is a valid editing target for multiple merged queries.
    Query merging concatenates several forget questions into one editing sample with one shared refusal target, assuming the same refusal is appropriate for all of them and that the editing method preserves reliability (Section 3.2).

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Cite this review

Pith. "Pith review of Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?." pith.science (2026). https://pith.science/paper/LF6ZWSGN

@misc{pith2026250519855,
  author       = {Pith},
  title        = {Pith review of: Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LF6ZWSGN}},
  note         = {Machine review of arXiv:2505.19855}
}
abstract

Large language Model (LLM) unlearning, i.e., selectively removing information from LLMs, is vital for responsible model deployment. Differently, LLM knowledge editing aims to modify LLM knowledge instead of removing it. Though editing and unlearning seem to be two distinct tasks, we find there is a tight connection between them. In this paper, we conceptualize unlearning as a special case of editing where information is modified to a refusal or "empty set" $\emptyset$ response, signifying its removal. This paper thus investigates if knowledge editing techniques are strong baselines for LLM unlearning. We evaluate state-of-the-art (SOTA) editing methods (e.g., ROME, MEMIT, GRACE, WISE, and AlphaEdit) against existing unlearning approaches on pretrained and finetuned knowledge. Results show certain editing methods, notably WISE and AlphaEdit, are effective unlearning baselines, especially for pretrained knowledge, and excel in generating human-aligned refusal answers. To better adapt editing methods for unlearning applications, we propose practical recipes including self-improvement and query merging. The former leverages the LLM's own in-context learning ability to craft a more human-aligned unlearning target, and the latter enables ROME and MEMIT to perform well in unlearning longer sample sequences. We advocate for the unlearning community to adopt SOTA editing methods as baselines and explore unlearning from an editing perspective for more holistic LLM memory control.

Figures

Figures reproduced from arXiv: 2505.19855 by the authors.

Figure 1
Figure 1. Illustrations of the connection between editing and unlearning for LLMs. A: Editing aims to alter the knowledge to a target. B: Unlearning tries to remove the knowledge and generate an "empty" (without information) answer. C: Editing as unlearning, can be done by editing that alters the knowledge into a target refusal answer. unlearning focuses on removing the knowledge, knowledge editing aims to alter the knowledge… view at source ↗
Figure 2
Figure 2. Methods of improving editing algorithms in unlearning settings. A: Self-improvement pipeline improves generalization and human value alignment for AlphaEdit and WISE. B: Query merging technique enables ROME and MEMIT to perform well under long unlearning sequences. Equation 3 bridges from editing to unlearning, making it applicable to verify whether editing meth￾ods are strong baselines for unlearning. 3.2 Improving… view at source ↗
Figure 3
Figure 3. Results of different numbers of forget sam￾ples. Factual dataset, Llama2-7B. 4.3 Comprehensive Analysis (RQ1 & RQ2) We study the capabilities of editing methods under rephrase attack and different numbers of forget samples. We note that the rephrase attack is noted as the generalization metric in knowledge editing (Wang et al., 2024a), and we use GPT-4 to synthesize the rephrased queries. For the figures, to get a m… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Comprehensive analysis of unlearning performances. The same setting as [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Case study of LLMs’ answers after unlearn￾ing. Factual dataset, Llama2-7B. 4.4 Improving Editing Methods in Unlearning Settings (RQ3) LLM outputs should align with human val￾ues (Wang et al., 2023). However, we observe that some unlearning methods cause models to gener…
Figure 6
Figure 6. Figure 6: Results of improving editing in unlearning. Factual dataset, Llama2-7B. Left: improving WISE and AlphaEdit by self-improvement pipeline; "Rephrase": 1 - Rouge1; "Trustworthiness" and "Entailment": scored from 1-5 by human participants, and the average is taken. Right: …

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BalDRO: A Distributionally Robust Optimization based Framework for Large Language Model Unlearning

    cs.LG 2026-01 conditional novelty 5.0 of 10

    BalDRO makes LLM unlearning more balanced by updating against a worst-case-weighted forget distribution, improving forget quality on TOFU/MUSE at stable utility.

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.