Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing

Jiakuan Xie; Jun Zhao; Kang Liu; Pengfei Cao; Yubo Chen

REVIEW 4 major objections 5 minor 42 references

Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Knowledge editing is often superficial: the original answer persists in later-layer attention heads, whose left singular vectors causally drive reversion, so ablating them weakens the old fact.

desk verdict Attack probes that contain the original answer make the headline prevalence claim uninterpretable, but the mechanistic investigation is substantial and worth salvaging with controls. read the letter →

arxiv 2505.12636 v1 pith:SLV7GNLG submitted 2025-05-19 cs.CL

Jiakuan Xie , Pengfei Cao , Yubo Chen , Kang Liu , Jun Zhao This is my paper · ORCID

classification cs.CL

keywords superficialeditingknowledgemechanisticinterpretabilityattentionheadssingularvaluedecompositionlogitlensresidualstreamunlearning

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

Knowledge editing can look flawless on standard metrics while the model still produces the original (pre-edit) answer when the prompt mentions the old fact; the paper calls this 'superficial editing' and shows it is widespread: several methods such as PMET and AlphaEdit revert on over 70% of the constructed stress-set cases. The paper's central claim is mechanistic: the old knowledge is not erased but stored in specific later-layer attention heads, and within those heads it lives in particular left singular vectors of the attention output matrix. Treating a head's output as a weighted sum of singular vectors, the paper shows that a few of these vectors alone decode to the original answer, and ablating them lowers the probability of the original answer while raising the probability of the new one. It also finds the same attention-head and singular-vector pattern in a superficially unlearned model, suggesting a shared circuit for residual knowledge. A sympathetic reader should care because the result identifies exactly where current editing algorithms fail and gives a concrete target for fixing them.

What carries the argument

The central object is the attention head's output matrix $W_O^{(l,h)}$ in later layers. The paper rewrites the head's contribution at the last token as $z = \sum_i \lambda_i u_i$, where $u_i$ are the left singular vectors of $W_O^{(l,h)}$ and $\lambda_i = \sigma_i v_i^\top x$ is a scalar coefficient set by the input; it claims a small subset of these $u_i$ carries the original answer. The accompanying mechanism is the 'Reversal of the Residual Stream' (RRS): from a late layer onward, the residual stream at the last token assigns higher probability to the original answer than to the edited answer, a reversal that is a prerequisite for superficial editing. A second load-bearing tool is the logit lens, which projects internal activations through the unembedding matrix to read latent probabilities of the original and new answers at each layer.

What would settle it

Recompute OM and OP on a random, unfiltered sample of CounterFact or ZsRE edits (no pre-filtering by reversion) and check whether the reversion rate is still above 70%; if it drops below, the claimed prevalence is an artifact of the stress-set construction.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is: current parameter-editing algorithms (ROME, MEMIT, MEND, PMET, r-ROME, AlphaEdit) leave the original knowledge functionally intact inside the Transformer. When the input is a baseline prompt, the edited model outputs the new fact; when the input is prefixed with a Wikipedia summary of the old answer, a repetition of the old answer, or a question containing the old triple, the model reverts to the old answer. This 'superficial editing' is mediated by two factors the paper validates: the residual stream at the last subject position in early layers, where enrichment of the new knowledge is suppressed, and, decisively, certain attention heads in later layers that inject the original answer into the final position. Decomposing those heads' output matrices by SVD, the paper isolates specific left singular vectors whose linear combination encodes the original answer; ablating the top 5–10% of these vectors decreases the original-answer probability and increases the new-answer probability across ROME- and MEMIT-edited LLaMA and Qwen models. The same vector pattern appears in a superficial-unlearning setup, supporting the conclusion that this is a general locus of 'knowledge that the model was supposed to lose.'

Load-bearing premise

The over-70% superficial-editing rate assumes that the filtered CF-a and ZsRE-a test sets, which keep only samples where ROME, MEMIT, or MEND already revert to the original answer, represent the real frequency of the phenomenon on ordinary knowledge edits; if those samples are not representative, the headline rate is an upper bound on a stress set, not a general prevalence estimate.

Editorial extensions

If this is right

Standard efficacy, generalization, and locality scores are not enough to certify an edit: near-perfect scores coexist with over 70% reversion on attack probes for PMET and AlphaEdit on the constructed stress sets.
The original fact is never fully overwritten by current parameter-editing methods; it is stored in later-layer attention heads, so editing at the MLP level alone leaves a live circuit for the old answer.
Because ablating the identified left singular vectors lowers the original-answer probability and raises the new-answer probability, a targeted intervention on those vectors is a concrete route to making edits stick.
The same attention-head and singular-vector signature appears in superficial unlearning, implying that 'forgetting' procedures also leave the targeted knowledge recoverable in the same circuit.

Reading between the lines

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

A testable design consequence: an editing or unlearning method that explicitly zeroes the coefficients of the identified left singular vectors at edit time should achieve lower OM/OP than algorithms that only adjust MLP weights; this is my proposal, not the paper's.
Because the datasets CF-a and ZsRE-a were filtered to samples that already revert, the over-70% figure is likely an upper bound for ordinary workloads; running the same metrics on an unfiltered random sample would give the true practical frequency.
The same SVD dissection could be applied to other residual behaviors, for example jailbreak susceptibility or fact contamination after safety fine-tuning, to test whether later-layer left singular vectors are a general repository of pre-intervention knowledge.
The three attack types are all built around the original object (Wikipedia summary, repetition, or a question with the old triple); testing paraphrasal or multi-hop attack prefixes would reveal whether the identified heads are the general locus of reversion or just one trigger.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here is the short version: the 70% superficial-editing figure should not be read as a prevalence estimate, and the attack probes used to define the phenomenon all contain the original answer, so the reversion may be ordinary in-context copying rather than leakage of stored knowledge. The mechanistic story about later-layer attention heads and singular vectors is plausible and worth a careful look, but it is currently under-controlled.

The paper does real work. It formalizes superficial editing, evaluates seven editing algorithms on three models across two datasets, and then runs a layered mechanistic investigation: residual-stream interventions, logit lens, head-level latent probabilities, SVD of head output matrices, and ablations. The consistency across ROME and MEMIT on three model families is a genuine strength, and the extension to unlearning is a sensible generalizability check. Releasing code is a plus.

The soft spot is load-bearing. Equation (2) defines attack prefixes as a Wikipedia summary of o, a repetition of o, or a question about o; Equation (3) then labels the edit superficial when the edited model outputs o on those prefixes. Because the unedited model already outputs o on the same strings, any model with a working copy circuit satisfies the definition. There is no distractor control where the prefix contains a different entity, and no unedited baseline, so the later-layer heads identified in Section 4.3 could simply be the circuit that moves an entity from context to the answer position, not components that encapsulate the original knowledge in the edited parameters. The dataset construction in Appendix A makes this worse: CF-a and ZsRE-a keep only samples that already revert under ROME, MEMIT, or MEND, so the OM/OP numbers in Table 1 are computed on a set guaranteed to contain the phenomenon. That makes the 'over 70%' claim partly circular. The causal sections would also benefit from random-head and matched-vector ablation controls, and the thresholds (tau=0.1, top-5%/10%) need sensitivity analysis. These are fixable, not fatal.

The paper is for people building knowledge-editing benchmarks and for interpretability researchers studying where parameter edits fail. As is, I would treat the mechanistic direction as promising rather than established. I would send it to peer review: the question is important, and a serious referee can push for the missing controls that would make the claims credible.

Referee Report

4 major / 5 minor

Summary. This paper defines 'superficial editing' as a failure mode in which a knowledge-edited language model answers the original object on attack probes that contain that original object, despite answering the new object on baseline prompts. It constructs two stress datasets (CF-a, ZsRE-a) by retaining samples that already revert under three attack types, evaluates seven editing methods on three instruction-tuned LLMs, and reports OM/OP metrics. The mechanistic study uses residual-stream patching, logit-lens analysis, and attention-head and left-singular-vector ablations to argue that earlier-layer residual streams are implicated in new-knowledge enrichment (H1) and that later-layer attention heads encode the original knowledge (H2). It extends the analysis to 'superficial unlearning' on a small set of RWKU-derived targets.

Significance. If the central claims were established, this would be a useful contribution: it provides a large-scale evaluation of editing algorithms under adversarial prompts and a concrete interpretability framework (residual-stream patching, head-level LOPH, SVD-based vector ablation) for a practically important failure mode. The paper is transparent about its dataset construction and limitations, and it claims to ship datasets and code. However, the validity of the headline prevalence numbers and the causal mechanism depends on controls that are currently missing, in particular a demonstration that the attack probes are not being satisfied by generic in-context copying. With those controls, the paper's stress-testing methodology would be a valuable addition to the knowledge-editing literature.

major comments (4)

[§2, Eq. (2)-(3); Appendix A] The attack prefixes defined in Eq. (2) are Wiki(o), Rep(o), and Que(o), each of which explicitly contains the original answer o. Equation (3) then classifies an edit as superficial whenever f'(a⊕x)=o, so any model that copies the most salient entity from the prefix satisfies the definition. Because the paper reports no control with a distractor entity in the prefix and no unedited-model baseline for the same probes, the OM/OP values in Table 1 and the mechanistic claims in §4.3.2–4.3.3 may reflect generic in-context copying rather than reversion to stored original knowledge. I request (i) unfiltered prevalence estimates on the original CounterFact/ZsRE inputs, (ii) a distractor-prefix control, and (iii) unedited-model baselines for the same probes.
[Appendix A; §3.1; Abstract] Appendix A states that CF-a and ZsRE-a are built by first editing with ROME, MEMIT, or MEND and then retaining only the samples that already elicit the original answer under the attack probes ('Samples that elicit the original answers from the model are retained'). The OM/OP numbers in Table 1 and the abstract's 'over 70% of cases' are therefore measured on a stress set that is guaranteed to contain the phenomenon; they do not estimate how often superficial editing occurs on unselected CounterFact or ZsRE inputs. Please report the rates on the unfiltered datasets, or explicitly qualify every prevalence claim as conditional on samples that were selected for revertibility.
[§4.3.2–4.3.3, Eq. (9), Eq. (11)] The causal identification of attention heads and singular vectors is circular because both are selected by their effect on the original answer's latent probability on the very same set of attack probes. Heads are chosen by LOPH (Eq. 9), and the top singular vectors are chosen by the drop in PLL(o|z) under ablation (Eq. 11); Tables 2 and 4 then show that ablating these selected components lowers OAP and raises NAP. Since the selection criterion is the same as the outcome, the result is expected by construction. Please validate the identified heads/vectors on a held-out set of attack types or on edits that do not exhibit superficial editing, and include an ablation of the same number of randomly chosen components as a control.
[Abstract; §1; Table 1] The abstract and Section 1 state that PMET and AlphaEdit 'exhibit superficial editing in over 70% of the cases.' In Table 1, this holds only for the Wiki attack on LLaMA3-8B-Instruct (PMET OM=70.33, AlphaEdit OM=72.53); for the Que attack the corresponding OM values are 39.29 and 34.52, and the appendix tables contain many lower values. The prevalence claim should be tied to the specific attack and model, and it should be based on the unfiltered datasets once those are computed.

minor comments (5)

[Appendix B, Eq. (15)] The locality definition is labelled 'Eff.' and the inequality uses o* instead of o, so the formula contradicts the prose definition of locality; please fix the label and the token in the inequality.
[§4.3.3 heading] 'Dissection of Attenion Head' should read 'Dissection of Attention Heads'.
[Table 1 and tables in Appendix B] The header 'OM ↓ OP↓' is ambiguous; please add a note that downward arrows indicate lower values are better, and ensure the arrow convention is applied consistently in the appendix tables.
[Abstract and §1] The code link is given as the word 'here' with no visible URL in the manuscript text; please provide the full URL in a footnote or reference.
[Appendix C.4] The unlearning experiments use only 26 targets and 50 samples; the main text should acknowledge this limited scale when claiming 'robustness and broader applicability.'

Circularity Check

2 steps flagged · score 6.0 of 10

Prevalence claim is measured on a dataset selected for the target outcome; head selection and ablation reuse the same target token.

self definitional [Section 2, Eqs. (3)-(4); Section 3.1; Appendix A step (2)]
"The edit is classified as superficial editing if the edited model f′ satisfies ... f′ (a⊕x) = o ... OM = E_x [f′ (a⊕x)=o] ... Samples that elicit the original answers from the model are retained for further analysis."

Equation (3) defines superficial editing as f′(a⊕x)=o, and Eq. (4) defines OM as E_x[f′(a⊕x)=o]. Appendix A then builds CF-a and ZsRE-a by retaining only samples where the edited model already elicits the original answer under the attack probe. The headline result that editing methods 'exhibit superficial editing in over 70% of the cases' is therefore measured on a dataset whose inclusion criterion is precisely the event being counted. The filter was applied using ROME, MEMIT, and MEND, so the PMET/AlphaEdit numbers are not literally forced, but the dataset is by construction an enriched stress set, not a random sample of CounterFact or ZsRE; reporting OM/OP on it as evidence of the phenomenon's prevalence is circular with the dataset definition.
fitted input called prediction [Section 4.3.2, Eq. (9) and Table 2; Section 4.3.3, Table 4]
"We derive the latent original probability of each head (LOPH): LOPH = P_LL(o| W_O^{(l,h)} x^{(l,h)}) ... we perform the corrupted run by zeroing the output of attention heads with LOPH values exceeding τ ... The results demonstrate a decrease in the probability of o, accompanied by a corresponding increase in the probability of o∗ after the removal of these attention heads."

The heads are selected because their output already decodes to the original answer o, and their causal role is then tested by measuring the drop in the probability of the same token o after ablation. Removing components that were chosen by their high P_LL(o) and observing a drop in o is a near-mechanical consequence of the selection rule rather than an independent test of stored original knowledge. Since every attack prefix in Eq. (2) contains o verbatim (Wiki(o), Rep(o), Que(o)), the same result is also compatible with a generic attention/copy circuit that moves the salient entity from the context into the final position; the claimed mechanism for 'encapsulating original knowledge' is not separated from this confound.

full rationale

Two load-bearing reductions are present. First, the CF-a and ZsRE-a datasets are constructed by retaining samples that already satisfy the definition of superficial editing, so the reported OM/OP percentages describe the constructed set rather than estimating prevalence on unfiltered CounterFact/ZsRE data; the headline 'over 70%' claim is thereby tied to its own selection criterion. Second, the mechanistic analysis selects attention heads by their latent probability of the original answer o (LOPH) and then ablates them and measures a drop in the same probability, making the causal evidence largely a restatement of the selection rule. Because all three attack prefixes in Equation (2) contain the original answer o verbatim, the observed behavior and the identified heads could equally reflect generic in-context copying of the salient entity from the prefix rather than reversion to stored original knowledge; no distractor-prefix control or unedited-model baseline is reported. The paper's core definition and metrics are internally consistent, and the residual-stream analysis (H1) has independent content, so the circularity is partial rather than total: 6.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities. The free parameters are thresholds and percentages chosen after inspecting the data. The axioms are standard interpretability assumptions about transformers, none of which are proven for these specific models. The dependence on Geva et al. (2023) for subject-position localization is a significant inherited assumption.

free parameters (5)

LOPH threshold tau = 0.1
Hand-set in Section 4.3.2 after balancing inclusion vs. noise; determines which attention heads are selected for ablation. No sensitivity analysis is reported.
Top-p singular vector percentage = 5% and 10%
Chosen in Section 4.3.3 for ablation and DSR; arbitrary and no sensitivity analysis is provided.
Top-K for DSR = 5, 10, 15
Used in Table 3 and Appendix C.3; these are arbitrary display values.
Repetition count m for Rep(o) attack = unspecified
Appendix A says the original answer is repeated m times but m is never given, making the attack impossible to reproduce exactly.
Unlearning LOPH threshold = 0.02
Set in Appendix C.4 for selecting heads in the superficial unlearning study; no justification or sensitivity analysis.

assumptions (5)

domain assumption The logit lens decodes intermediate hidden states into meaningful vocabulary-space probabilities.
Used in Eq. (16) and throughout; a well-known but heuristic interpretability tool, not a proven guarantee.
domain assumption The last subject position in earlier layers is the locus of factual recall (subject enrichment).
Inherited from Geva et al. (2023); the paper's H1 relies on this localization.
domain assumption The attack prefixes Wiki(o), Rep(o), Que(o) capture realistic contexts that induce reversion.
The whole evaluation rests on this; the paper's own Limitations section admits three contexts may not cover all scenarios.
domain assumption When the edited model outputs the original answer under an attack probe, this indicates the original knowledge survived the edit, rather than being an artifact of prompt confusion.
The definition of superficial editing in Eq. (3) presumes the original knowledge is still encoded; no control verifies this on a per-sample basis.
standard math SVD of attention output matrices yields directions that are semantically interpretable.
SVD is standard linear algebra; projecting via logit lens is the interpretability step, which is a domain assumption.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing." pith.science (2026). https://pith.science/paper/SLV7GNLG

@misc{pith2026250512636,
  author       = {Pith},
  title        = {Pith review of: Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SLV7GNLG}},
  note         = {Machine review of arXiv:2505.12636}
}

read the original abstract

Knowledge editing, which aims to update the knowledge encoded in language models, can be deceptive. Despite the fact that many existing knowledge editing algorithms achieve near-perfect performance on conventional metrics, the models edited by them are still prone to generating original knowledge. This paper introduces the concept of "superficial editing" to describe this phenomenon. Our comprehensive evaluation reveals that this issue presents a significant challenge to existing algorithms. Through systematic investigation, we identify and validate two key factors contributing to this issue: (1) the residual stream at the last subject position in earlier layers and (2) specific attention modules in later layers. Notably, certain attention heads in later layers, along with specific left singular vectors in their output matrices, encapsulate the original knowledge and exhibit a causal relationship with superficial editing. Furthermore, we extend our analysis to the task of superficial unlearning, where we observe consistent patterns in the behavior of specific attention heads and their corresponding left singular vectors, thereby demonstrating the robustness and broader applicability of our methodology and conclusions. Our code is available here.

Figures

Figures reproduced from arXiv: 2505.12636 by the authors.

**Figure 2.** Intervention results of LLaMA3-8B-Instruct [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Latent probabilities of the original answer for [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (22 more)

**Figure 6.** Figure 6: Intervention effects following critical atten [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 5.** Figure 5: The rankings of o and o ∗ in the latent probability distribution at the last subject token for LLaMA3- 8B-Instruct edited by ROME and MEMIT. Results for other models are provided in Appendix C.2. ranking of the original answer consistently falls behind that of the ne…

**Figure 7.** Figure 7: LOPH of LLaMA3-8B-Instruct edited by ROME and MEMIT. Results for other models are provided in Appendix C.3. Models Methods Original New w/o abl. abl. ↓ ∆P w/o abl. abl. ↑ ∆P LLaMA3- 8B-Instruct ROME 57.17 35.58 21.59 16.49 20.71 4.22 MEMIT 56.90 37.36 19.54 15.68 18.3…

**Figure 8.** Figure 8: Average LOPH of the unlearned LLaMA3.2- 3B-Instruct models. Setting w/o abl. -top 5% -top 10% Probability 53.95 35.12 28.97 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

**Figure 9.** Figure 9: The prompt for Qwen2.5-32B-Instruct to gen [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

**Figure 10.** Figure 10: Dataset Wiki Rep Que Total CF-a 323 484 204 1011 ZsRE-a 133 214 122 469 [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 11.** Figure 11: Intervention results of Qwen2.5-7B-Instruct edited by ROME at different tokens. [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]

**Figure 12.** Figure 12: Intervention results of Qwen2.5-7B-Instruct edited by MEMIT at different tokens. [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]

**Figure 13.** Figure 13: Intervention results of Qwen2.5-14B-Instruct edited by ROME at different tokens. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]

**Figure 14.** Figure 14: Intervention results of Qwen2.5-14B-Instruct edited by MEMIT at different tokens. [PITH_FULL_IMAGE:figures/full_fig_p016_14.png]

**Figure 15.** Figure 15: The latent probabilities of o for the input and output of MLP and Attention output matrix in Qwen2.5- 7B-Instruct edited by ROME. 0 5 10 15 20 25 Layer 0.0 0.1 0.2 0.3 0.4 0.5 Latent Probability Input Output (a) Results of MLP. 0 5 10 15 20 25 Layer 0.00 0.05 0.10 0.1…

**Figure 16.** Figure 16: The latent probabilities of o for the input and output of MLP and Attention output matrix in Qwen2.5- 7B-Instruct edited by MEMIT. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_16.png]

**Figure 17.** Figure 17: The latent probabilities of o for the input and output of MLP and Attention output matrix in Qwen2.5- 14B-Instruct edited by ROME. 0 10 20 30 40 Layer 0.0 0.2 0.4 0.6 Latent Probability Input Output (a) Results of MLP. 0 10 20 30 40 Layer 0.00 0.05 0.10 0.15 0.20 0.25…

**Figure 18.** Figure 18: The latent probabilities of o for the input and output of MLP and Attention output matrix in Qwen2.5- 14B-Instruct edited by MEMIT. 0 5 10 15 20 25 Layer 0.0 2.5 5.0 7.5 10.0 12.5 15.0 Inhibition Score Corrupted run Clean run (a) ROME. 0 5 10 15 20 25 Layer 0 2 4 6 8 …

**Figure 19.** Figure 19: The suppression results for Qwen2.5-7B-Instruct edited by ROME and MEMIT. [PITH_FULL_IMAGE:figures/full_fig_p017_19.png]

**Figure 20.** Figure 20: The suppression results for Qwen2.5-14B-Instruct edited by ROME and MEMIT. [PITH_FULL_IMAGE:figures/full_fig_p018_20.png]

**Figure 21.** Figure 21: The ranking of o and o ∗ in the latent probability distribution at the last subject position for Qwen2.5-7BInstruct edited by ROME and MEMIT. 0 10 20 30 40 Layer 10 4 10 5 Ranking (log scale) Original Answer New Answer (a) ROME. 0 10 20 30 40 Layer 10 3 10 4 10 5 Ran…

**Figure 22.** Figure 22: The ranking of o and o ∗ in the latent probability distribution at the last subject position for Qwen2.5- 14B-Instruct edited by ROME and MEMIT. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_22.png]

**Figure 23.** Figure 23: Intervention effects following critical attention module ablation in Qwen2.5-7B-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p019_23.png]

**Figure 24.** Figure 24: Intervention effects following critical attention module ablation in Qwen2.5-14B-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p019_24.png]

**Figure 25.** Figure 25: LOPH of Qwen2.5-7B-Instruct edited by ROME and MEMIT. [PITH_FULL_IMAGE:figures/full_fig_p019_25.png]

**Figure 26.** Figure 26: LOPH of Qwen2.5-14B-Instruct edited by ROME and MEMIT. [PITH_FULL_IMAGE:figures/full_fig_p020_26.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 6 canonical work pages

[1]

Yuchen Cai and Ding Cao. 2024. https://arxiv.org/abs/2410.11469 O-edit: Orthogonal subspace editing for language model sequential editing . Preprint, arXiv:2410.11469

arXiv 2024
[2]

Ruizhe Chen, Yichen Li, Jianfei Yang, Joey Tianyi Zhou, and Zuozhu Liu. 2024. https://arxiv.org/abs/2408.11843 Editable fairness: Fine-grained bias mitigation in language models . Preprint, arXiv:2408.11843

arXiv 2024
[3]

Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2024. https://doi.org/10.1162/tacl_a_00644 Evaluating the ripple effects of knowledge editing in language models . Transactions of the Association for Computational Linguistics, 12:283--298

doi:10.1162/tacl_a_00644 2024
[4]

Guy Dar, Mor Geva, Ankit Gupta, and Jonathan Berant. 2023. https://doi.org/10.18653/v1/2023.acl-long.893 Analyzing transformers in embedding space . In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16124--16170, Toronto, Canada. Association for Computational Linguistics

doi:10.18653/v1/2023.acl-long.893 2023
[5]

Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021. Editing factual knowledge in language models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6491--6506, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics

work page 2021
[6]

Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2...

2021
[7]

Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Xiang Wang, Xiangnan He, and Tat seng Chua. 2024. https://arxiv.org/abs/2410.02355 Alphaedit: Null-space constrained knowledge editing for language models . Preprint, arXiv:2410.02355

arXiv 2024
[8]

Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.751 Dissecting recall of factual associations in auto-regressive language models . In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12216--12235, Singapore. Association for Computational Linguistics

doi:10.18653/v1/2023.emnlp-main.751 2023

Show all 42 references

[9]

Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Goldberg. 2022. https://doi.org/10.18653/v1/2022.emnlp-main.3 Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space . In Proceedings of the 2022 Conference on Empirical Methods in Natural L...

2022 doi
[10]

Akshat Gupta, Sidharth Baskaran, and Gopala Anumanchipalli. 2024. https://doi.org/10.18653/v1/2024.emnlp-main.1210 Rebuilding ROME : Resolving model collapse during sequential model editing . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Proces...

2024 doi
[11]

Danny Halawi, Jean-Stanislas Denain, and Jacob Steinhardt. 2024. https://arxiv.org/abs/2307.09476 Overthinking the truth: Understanding how language models process false demonstrations . Preprint, arXiv:2307.09476

2024 arXiv
[12]

Tom Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2023. https://proceedings.neurips.cc/paper_files/paper/2023/file/95b6e2ff961580e03c0a662a63a71812-Paper-Conference.pdf Aging with grace: Lifelong model editing with discrete key-value adapto...

2023
[13]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2016
[14]

Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. 2022. https://arxiv.org/abs/2210.01504 Knowledge unlearning for mitigating privacy risks in language models . Preprint, arXiv:2210.01504

2022 arXiv
[15]

Houcheng Jiang, Junfeng Fang, Tianyu Zhang, An Zhang, Ruipeng Wang, Tao Liang, and Xiang Wang. 2024. https://arxiv.org/abs/2410.04045 Neuron-level sequential editing for large language models . Preprint, arXiv:2410.04045

2024 arXiv
[16]

Zhuoran Jin, Pengfei Cao, Chenhao Wang, Zhitao He, Hongbang Yuan, Jiachun Li, Yubo Chen, Kang Liu, and Jun Zhao. 2024. https://arxiv.org/abs/2406.10890 Rwku: Benchmarking real-world knowledge unlearning for large language models . Preprint, arXiv:2406.10890

2024 arXiv
[17]

Xiaopeng Li, Shasha Li, Shezheng Song, Jing Yang, Jun Ma, and Jie Yu. 2024. https://doi.org/10.1609/aaai.v38i17.29818 Pmet: Precise model editing in a transformer . Proceedings of the AAAI Conference on Artificial Intelligence, 38(17):18564--18572

2024 doi
[18]

Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, and Dylan Hadfield-Menell. 2024. https://arxiv.org/abs/2402.16835 Eight methods to evaluate robust unlearning in llms . Preprint, arXiv:2402.16835

2024 arXiv
[19]

Xinbei Ma, Tianjie Ju, Jiyang Qiu, Zhuosheng Zhang, Hai Zhao, Lifeng Liu, and Yulong Wang. 2024. https://doi.org/10.18653/v1/2024.emnlp-main.906 On the robustness of editing large language models . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language ...

2024 doi
[20]

Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. https://proceedings.neurips.cc/paper_files/paper/2022/file/6f1d43d5a82a37e89b0665b33bf3a182-Paper-Conference.pdf Locating and editing factual associations in gpt . In Advances in Neural Information Processing Sy...

2022
[21]

Kevin Meng, Arnab Sen Sharma, Alex J Andonian, Yonatan Belinkov, and David Bau. 2023. https://openreview.net/forum?id=MkbcAHIYgyS Mass-editing memory in a transformer . In The Eleventh International Conference on Learning Representations

2023
[22]

Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. 2022 a . https://openreview.net/forum?id=0DcZxeWfOPt Fast model editing at scale . In International Conference on Learning Representations

2022
[23]

Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn. 2022 b . https://proceedings.mlr.press/v162/mitchell22a.html Memory-based model editing at scale . In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proc...

2022
[24]

nostalgebraist. 2020. https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens Interpreting gpt: the logit lens

2020
[25]

Domenic Rosati, Robie Gonzales, Jinkun Chen, Xuemin Yu, Yahya Kayani, Frank Rudzicz, and Hassan Sajjad. 2024. https://doi.org/10.18653/v1/2024.naacl-long.208 Long-form evaluation of model editing . In Proceedings of the 2024 Conference of the North American Chapter of the Asso...

2024 doi
[26]

Atakan Seyitoğlu, Aleksei Kuvshinov, Leo Schwinn, and Stephan Günnemann. 2024. https://arxiv.org/abs/2411.02631 Extracting unlearned information from llms with activation steering . Preprint, arXiv:2411.02631

2024 arXiv
[27]

Rheeya Uppaal, Apratim Dey, Yiting He, Yiqiao Zhong, and Junjie Hu. 2024. https://arxiv.org/abs/2405.13967 Model editing as a robust and denoised variant of dpo: A case study on toxicity . Preprint, arXiv:2405.13967

2024 arXiv
[28]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf Attention is all you need . In Advances in Ne...

2017
[29]

Chenhao Wang, Pengfei Cao, Zhuoran Jin, Yubo Chen, Daojian Zeng, Kang Liu, and Jun Zhao. 2024 a . https://doi.org/10.18653/v1/2024.acl-long.732 MULFE : A multi-level benchmark for free text model editing . In Proceedings of the 62nd Annual Meeting of the Association for Comput...

2024 doi
[30]

Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu, and Fandong Meng. 2024 b . https://doi.org/10.18653/v1/2024.acl-long.627 Cross-lingual knowledge editing in large language models . In Proceedings of the 62nd Annual Meeting of the Association for Computational Li...

2024 doi
[31]

Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, and Huajun Chen. 2024 c . https://doi.org/10.18653/v1/2024.acl-long.171 Detoxifying large language models via knowledge editing . In Proceedings of the 62nd Annual M...

2024 doi
[32]

Xiaobao Wu, Liangming Pan, William Yang Wang, and Anh Tuan Luu. 2024. https://doi.org/10.18653/v1/2024.emnlp-main.843 AKEW : Assessing knowledge editing in the wild . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15118--15133,...

2024 doi
[33]

Yang Xu, Yutai Hou, Wanxiang Che, and Min Zhang. 2023. https://doi.org/10.18653/v1/2023.findings-acl.343 Language anisotropic cross-lingual model editing . In Findings of the Association for Computational Linguistics: ACL 2023, page 5554–5569. Association for Computational Linguistics

2023 doi
[34]

Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, and Xueqi Cheng. 2024. https://doi.org/10.18653/v1/2024.findings-acl.322 The butterfly effect of model editing: Few edits can trigger large language models collapse . In Findings of the Association for Computational Linguistic...

2024 doi
[35]

Hongbang Yuan, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, and Jun Zhao. 2024. https://arxiv.org/abs/2408.10682 Towards robust knowledge unlearning: An adversarial framework for assessing and improving unlearning robustness in large language models . Preprint, arXiv:2408.10682

2024 arXiv
[36]

Zhiwei Zhang, Fali Wang, Xiaomin Li, Zongyu Wu, Xianfeng Tang, Hui Liu, Qi He, Wenpeng Yin, and Suhang Wang. 2025. https://arxiv.org/abs/2410.16454 Catastrophic failure of llm unlearning via quantization . Preprint, arXiv:2410.16454

2025 arXiv
[37]

Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.296 Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Pro...

2023 doi
[38]

Zexuan Zhong, Zhengxuan Wu, Christopher Manning, Christopher Potts, and Danqi Chen. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.971 MQ u AKE : Assessing knowledge editing in language models via multi-hop questions . In Proceedings of the 2023 Conference on Empirical Meth...

2023 doi
[39]

Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and Sanjiv Kumar. 2020. https://arxiv.org/abs/2012.00363 Modifying memories in transformer models . Preprint, arXiv:2012.00363

2020 arXiv
[40]

Zico Kolter, and Matt Fredrikson

Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. 2023. https://arxiv.org/abs/2307.15043 Universal and transferable adversarial attacks on aligned language models . Preprint, arXiv:2307.15043

2023 arXiv
[41]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...
[42]

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 15, 2026 · model on record in the stance chip above.

[1] [1]

Yuchen Cai and Ding Cao. 2024. https://arxiv.org/abs/2410.11469 O-edit: Orthogonal subspace editing for language model sequential editing . Preprint, arXiv:2410.11469

arXiv 2024

[2] [2]

Ruizhe Chen, Yichen Li, Jianfei Yang, Joey Tianyi Zhou, and Zuozhu Liu. 2024. https://arxiv.org/abs/2408.11843 Editable fairness: Fine-grained bias mitigation in language models . Preprint, arXiv:2408.11843

arXiv 2024

[3] [3]

Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2024. https://doi.org/10.1162/tacl_a_00644 Evaluating the ripple effects of knowledge editing in language models . Transactions of the Association for Computational Linguistics, 12:283--298

doi:10.1162/tacl_a_00644 2024

[4] [4]

Guy Dar, Mor Geva, Ankit Gupta, and Jonathan Berant. 2023. https://doi.org/10.18653/v1/2023.acl-long.893 Analyzing transformers in embedding space . In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16124--16170, Toronto, Canada. Association for Computational Linguistics

doi:10.18653/v1/2023.acl-long.893 2023

[5] [5]

Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021. Editing factual knowledge in language models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6491--6506, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics

work page 2021

[6] [6]

Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2...

2021

[7] [7]

Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Xiang Wang, Xiangnan He, and Tat seng Chua. 2024. https://arxiv.org/abs/2410.02355 Alphaedit: Null-space constrained knowledge editing for language models . Preprint, arXiv:2410.02355

arXiv 2024

[8] [8]

Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.751 Dissecting recall of factual associations in auto-regressive language models . In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12216--12235, Singapore. Association for Computational Linguistics

doi:10.18653/v1/2023.emnlp-main.751 2023