Pith. sign in

REVIEW 5 major objections 5 minor 1 cited by

Agents Are All You Need for LLM Unlearning

T0 review · 5 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read A multi-agent pipeline that filters LLM outputs at inference time can unlearn targeted knowledge without touching model weights, matching or beating fine-tuning-based unlearning.

desk verdict Novel multi-agent filter, but its WMDP win is built into the protocol and its main judge is its own critic; the framework deserves a tougher evaluation, not a desk reject. read the letter →

arxiv 2502.00406 v2 pith:G6L5Z6VS submitted 2025-02-01 cs.AI cs.CL

Debdeep Sanyal , Murari Mandal This is my paper · ORCID
classification cs.AIcs.CL
keywords LLMunlearninginference-timemulti-agentframeworkposthocmodel-agnosticjailbreakrobustnessscalability
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

This paper argues that a modular team of LLM agents, applied at inference time, may be all that is needed for effective and practical LLM unlearning. The proposed framework, ALU, takes a user query and a list of unlearning targets, lets a 'vanilla' agent answer freely, then runs the answer through three more agents that detect references to the targets, generate sanitized variants, rate them, and compose a final response that leaks nothing about the targets. Because no weights are updated, unlearning can be requested on the fly, at near-constant time cost, and without access to the model's internals. Across TOFU, WMDP, WPU, and MMLU, the paper reports that ALU outperforms optimization-based and post hoc baselines in both unlearning efficacy and retained utility, while remaining robust to jailbreaking attempts and scaling to 1000 targets.

What carries the argument

The load-bearing object is the four-agent filtering pipeline. The Vanilla Agent first answers the query with no filtering, acting as a shock absorber that keeps jailbreaks from shaping the information that later agents see. The AuditErase Agent locates any direct or indirect mention of the unlearning targets in the vanilla response and produces k=5 sanitized variations of the response. The Critic Agent, implemented with GPT-4o, rates each variation on a 1–5 scale for both target removal and retained utility, and the Composer Agent selects the top-j rated responses, synthesizes the final answer if their mean score meets a threshold, and otherwise returns a refusal. This decomposition is what lets the framework edit entangled knowledge without altering the base model's weights.

What would settle it

Prompt the underlying base model directly, bypassing ALU, with a question about a target the pipeline was asked to unlearn; if the base model answers with that target's name or details, the framework has filtered the response without removing the knowledge. A complementary check is to compare the base model's embeddings or next-token distributions for forget-set prompts before and after ALU runs: since no weights change, they will be identical, showing the unlearning is response-level rather than model-level.

Watch

Extended reading notes

Core claim

The paper's central claim is that agents might be all we need for effective and practical inference-time LLM unlearning, and that the proposed ALU framework is the most robust inference-time unlearning method among current state-of-the-art approaches. ALU treats unlearning as a response-filtering task: a vanilla agent produces the model's natural answer, an AuditErase agent identifies direct or indirect references to any of the user-specified targets and generates k sanitized rewrites, a Critic agent scores each rewrite for both leakage and utility, and a Composer agent merges the top-scoring rewrites into a final answer, falling back to a refusal only when the scores fall below a threshold. This pipeline requires no weight updates for any of its agents, so a user can sequence any set of unlearning requests in real time, and the reported time cost stays effectively constant as the target list grows. On the TOFU, WMDP, and WPU benchmarks, ALU is reported to achieve the strongest balance of forgetting and retention, driving WMDP multiple-choice accuracy to near random guessing while keeping MMLU utility close to the original model.

Load-bearing premise

The whole framework assumes that users can only talk to the model through the agent pipeline; if anyone can query the underlying base model directly, the 'unlearned' information is still sitting in its weights and comes right back.

Editorial extensions

If this is right

  • Unlearning becomes a runtime service: targets can be added, removed, or reordered between queries with no training or weight access.
  • The reported near-constant time cost means forget sets of hundreds or thousands of targets can be handled without proportional slowdown.
  • Because the pipeline is model-agnostic, the same agent team can be wrapped around open-weight and proprietary LLMs alike.
  • The method's robustness to target masking, multilingual prompts, and many-shot jailbreaking suggests it can serve as a safety layer in deployment.
  • The strong retain scores on TOFU and WPU indicate that response-level filtering can preserve utility better than weight-updating approaches that trade off forgetting and retention.

Reading between the lines

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

  • The unlearning guarantee is only as strong as the interface: an adversary who can prompt the underlying base model directly, bypassing the pipeline, would recover the target information, because the weights are untouched. The paper acknowledges this black-box assumption.
  • The framework's empirical edge depends on the attack suite used; a jailbreak that tampers with the Critic or Composer agents (for example, through system-prompt injection) could defeat the filtering, since those agents are themselves LLMs.
  • The same four-agent recipe could be repurposed for other output-filtering duties, such as redacting personal data or enforcing content policies, since the core is target detection and response rewriting rather than anything specific to unlearning.
  • Reported time is constant in target count but still scales with the number of agents and sampled rewrites, so the practical cost is per-query latency and API calls rather than training compute.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper proposes ALU, an agentic inference-time LLM unlearning framework in which four specialized agents (Vanilla, AuditErase, Critic, Composer) sequentially filter a base model's responses so that references to user-specified targets are removed or suppressed, without any weight updates. The authors evaluate ALU on TOFU, WMDP, and WPU, compare it with optimization-based methods (RMU, NPO, SNAP, etc.) and post hoc methods (ICUL, Guardrail), measure utility on MMLU, test robustness to several jailbreaking perturbations, and claim near-constant time cost with scalability to 1000 targets. The central claim is that multi-agent inference-time filtering 'might be all we need for effective and practical inference-time LLM unlearning.'

Significance. If the central claims held, the paper would be a useful contribution to black-box, dynamic unlearning: it presents a modular pipeline with explicit prompts, evaluates across 31 models, includes ablations, and attempts a scale beyond prior work. The TOFU and WPU similarity results show large reductions in forget-set similarity with generally preserved utility, which is a genuine empirical result if the metrics are computed as described. However, the WMDP evaluation relies on a hardcoded random-answer fallback, and the privacy evaluation uses the same model as both sanitizer and judge, so the headline comparative and robustness claims are not yet supported. The paper is honest about its black-box threat model in Appendices A.3 and A.4, but that honesty also delimits the contribution: ALU is an interface-level filtering system rather than a method that removes information from the underlying model.

major comments (5)
  1. [§4.1, Appendix A.5, Table 1] The WMDP scores in Table 1 and Table 32 are not evidence of unlearning because Appendix A.5 states that when AuditErase identifies a target reference, the framework bypasses the other agents and prompts a separate agent to choose a random option from the four choices. Near-random accuracy is therefore a guaranteed outcome of this rule, not a measured property of the ALU pipeline or the base model. This makes the comparison with RMU, SNAP, SSD, and the other optimization-based methods in Section 4.1 unsupported: ALU is being compared on a different task (random-option selection after target detection) rather than on unlearning of hazardous knowledge. The authors should report the frequency of fallback invocation, the accuracy on questions where no fallback was triggered, and the target detection rate of AuditErase, or remove the WMDP comparison from the central claims.
  2. [§3, §4.3, Appendix A.5] The privacy evaluation is self-referential: the GPT Privacy Score used to measure leakage in Table 3 is computed by GPT-4o (Appendix A.5), and the Critic agent inside ALU is also GPT-4o (Section 3). The same model that sanitizes the responses is the model that judges whether leakage occurred, so the high scores may reflect the judge's own sanitization criteria rather than the actual absence of target information. The paper should re-run the privacy evaluations with an independent judge model or a human adversarial evaluation and should, at minimum, disclose this circularity in the main text.
  3. [§4.2, Table 2] The claim in Section 4.2 that ALU 'consistently outperforms' ICUL and Guardrail 'in terms of both unlearning and retaining' is contradicted by the WMDP-chem rows of Table 2: Guardrail has higher Retain cosine similarity (0.594 vs 0.572) and higher Retain ROUGE (0.609 vs 0.560) than ALU. The subsequent discussion of a 'minor Retain score decrease' does not reconcile the categorical 'both unlearning and retaining' wording, which should be corrected.
  4. [§4.3, Appendix A.8] The controlled-experiment results in Table 3 are based on a biased protocol: Appendix A.8 states that each question was evaluated three times and 'the best score of the 3 was considered' before averaging over the three questions per perturbation. Best-of-k scoring inflates expected scores and obscures variance, so the reported robustness advantages are not statistically grounded; the authors should report mean or median with standard deviations over repeated runs.
  5. [§A.1, Table 5] The 'near-constant time' claim is not supported by the evidence presented. Table 5 lists 'α' for ALU at 20, 40, and 100 targets and 36 seconds at 200 targets, so no scaling trend is actually shown. Moreover, Algorithm 1 (Step 2) requires AuditErase to compare the vanilla response against every target in the list, so the input context and hence inference cost must grow with the number of targets. The authors should measure end-to-end latency over a range of target-list sizes and either substantiate or remove the constant-time claim.
minor comments (5)
  1. [§4.1, Table 1] The phrase 'almost perfect score on MMLU' is not supported by Table 1, where ALU's MMLU accuracy is 57.64 versus the original 58.94 (a 1.3-point drop); please use a more precise description.
  2. [Appendix A.7] The heading 'AuditErase Agent' appears twice; the second prompt (starting 'You are a helpful assistant, and you will be provided with a list of responses...') appears to be the Composer prompt and should be relabeled.
  3. [Tables 24 and 27] Table 24 (falcon-10b instruct, ICUL WMDP ROUGE Pre-UL) reports a value of 1.014, and Table 27 (Qwen2.5-3B) reports cosine values of 1.006 and 1.014; these exceed the theoretical range of these metrics and should be corrected.
  4. [§2, Appendix A.11] The reference to SNAP as 'Sarlin et al. (2023)' in Section 2 and Appendix A.11 is to a visual-positioning paper; the described SNAP unlearning method appears to be from a different source, so the citation should be corrected.
  5. [Appendix A.2, A.1, A.3] The statement in Appendix A.2 that ALU 'poses virtually no risk of information leakage' is in tension with the admission in Appendix A.1 of 'a few indirect references around 1000 targets' and with the false-positive behavior discussed in A.3; the claims should be reconciled.

Circularity Check

2 steps flagged · score 7.0 of 10

ALU's headline WMDP unlearning scores are installed by the random-answer fallback in Appendix A.5, and its robustness is self-certified by GPT-4o judging GPT-4o, so the central 'most robust unlearning' claim is substantially circular.

  1. self definitional [Appendix A.5, Evaluation Metrics, item 4 (Multiple-choice accuracy); Tables 1 and 32]
    "Although ALU is not primarily designed to support this assumption, we made a minor modification to the framework to adjust ALU for this. Specifically, on identification of references to any subject in the AuditErase agent, we bypass the other agents in the framework and prompt a separate agent to choose a random option from the provided set of options. As both the WMDP and MMLU datasets consist of multiple-choice questions with four possible options, achieving an accuracy close to 0.25 indicates successful unlearning within the context of these datasets."

    The headline WMDP forget accuracies (Table 1: 26.31 / 25.12 / 24.76, and all entries under 28.0 in Table 32) are the direct output of the random-option fallback, not a measured property of an unlearned model. Whenever AuditErase detects a target, ALU is hardcoded to bypass all other agents and guess randomly, so ~25% accuracy is installed by the evaluation protocol. The paper then presents this as evidence that ALU 'consistently outperforms' optimization-based methods and is the 'most robust' unlearning framework. The measurement reduces to the protocol input: the observed 'unlearned' score is equivalent to 'the fallback fired and guessed'; it provides no independent evidence that information was removed or suppressed by the agent pipeline.

  2. other [Section 3 (Critic agent) and Appendix A.5 (GPT Privacy Score); Table 3]
    "To address this limitation, we include a critic agent Mc with GPT-4o as the critic to ensure an unbiased and thorough evaluation of the responses. ... We provide the original user query, along with the response(s) from the framework and the forget set to GPT-4o and prompt it to analyze the framework response(s) for any reference to one or multiple targets from the forget set and based on its analysis, rate the responses in the range [1, 5]."

    The leakage detector used to certify ALU's robustness (GPT Privacy Score in Table 3) is GPT-4o, the same model that, as the Critic agent, decides what counts as leakage inside ALU and steers final response selection. Both the evaluation and the filter receive the same forget set and the same query/response, and both rely on GPT-4o's single, shared judgment of what constitutes an 'indirect reference.' If GPT-4o's notion of leakage misses a subtle mention during the Critic step, the GPT Privacy Score will miss it too; the two procedures are the same function applied to the same inputs.

full rationale

The most load-bearing comparative evidence for ALU's unlearning efficacy is the WMDP multiple-choice accuracy in Tables 1 and 32. Appendix A.5 shows that this result is manufactured by the evaluation protocol: whenever AuditErase identifies a target, ALU bypasses all agents and picks a random option. Near-random accuracy is therefore a designed property of the harness, not a measured effect of the agentic unlearning pipeline, which makes the central 'consistently outperforms' claim partly circular. In addition, the paper's robustness evaluation (Table 3) uses GPT Privacy Score, where GPT-4o is the judge, while GPT-4o is also the Critic agent inside ALU. The filter and the judge are identical, so the robustness scores are self-referential rather than obtained against an independent oracle. The paper is transparent about its interface-level threat model: A.4 explicitly redefines unlearning as preventing leakage through a response-level 'switch' and A.3 assumes users have no access to internal agents or the base model. That redefinition makes the claim 'ALU unlearns' true only within its own definition of unlearning as output filtering; the underlying weights are never changed, and anyone with direct base-model access recovers the information. Not all results are circular: the MMLU utility numbers, the retain ROUGE scores, and the runtime/scalability measurements are externally meaningful and are not produced by the random-answer fallback. But because the flagship forget-set comparison is forced by the protocol and the robustness claim is self-judged, the paper's central 'most robust inference-time LLM unlearning framework' conclusion is substantially, though not entirely, circular. Score 7 reflects one prediction that reduces by construction plus a self-referential evaluation, while acknowledging that independent utility and scalability evidence does exist.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The only new conceptual objects are the agent roles themselves, which are not new entities but configurations of existing LLMs. The four agents and the random-choice bypass agent are procedural components, not postulates of new physical or mathematical quantities. The free parameters and domain assumptions above are what the central claims actually rest on.

free parameters (5)
  • k (sanitized response variations per target) = 5
    Set in Section 3 and justified by Figure 2 as a utility and efficacy trade-off; tuned on TOFU retain ROUGE, so it is a free parameter fitted to evaluation data.
  • j (number of top-rated responses used by Composer) = not reported
    Algorithm 1 uses Top-j but the paper never states the value of j, leaving a key component of the final response unspecified.
  • Composer threshold for mean critic score = 4
    Algorithm 1: if S >= 4 then Rfinal <- Mcp(Rt) else Rfinal <- phi. This hand-set threshold controls how often ALU defaults to a refusal, directly shaping utility and leakage.
  • WMDP random-option bypass rule = random choice among 4 options when target detected
    Appendix A.5: when AuditErase detects a target, a separate agent chooses a random option. This rule mechanically produces near-25% accuracy and makes the WMDP unlearning result a consequence of the protocol.
  • Few-shot examples per agent = 7-10, not disclosed
    Appendix A.7 says 7-10 examples were empirically determined but the examples are missing from the manuscript, so the prompt configuration cannot be reproduced.
assumptions (4)
  • domain assumption A single LLM pass by the AuditErase agent can reliably identify direct and indirect references to any target in a response, including indirect references, across up to 1000 targets.
    Section 3, AuditErase agent; the paper's own A.3 admits smaller models produce false positives, so this reliability is not guaranteed and is load-bearing for preventing leakage.
  • domain assumption Adversaries cannot obtain information except through ALU's filtered response interface; the base model's retained knowledge is inaccessible via side channels.
    Section A.3 and A.4: the paper assumes a black-box setting and argues post hoc filtering satisfies unlearning; if false, the unlearned information is trivially recoverable from the unchanged base model.
  • domain assumption GPT-4o's ratings are an unbiased measure of information leakage and response utility, so it can serve both as the Critic inside ALU and as the privacy-score judge.
    Section 3 (Critic) and Appendix A.5 (GPT Privacy Score); the overlap makes the evaluation self-referential.
  • domain assumption Near-random multiple-choice accuracy is a valid indicator of successful unlearning.
    Appendix A.5; the assumption underlies the WMDP evaluation, but ALU's near-random scores are produced by a random-guess rule, not by absence of knowledge.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Agents Are All You Need for LLM Unlearning." pith.science (2026). https://pith.science/paper/G6L5Z6VS

@misc{pith2026250200406,
  author       = {Pith},
  title        = {Pith review of: Agents Are All You Need for LLM Unlearning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G6L5Z6VS}},
  note         = {Machine review of arXiv:2502.00406}
}
read the original abstract

Information removal or suppression in large language models (LLMs) is a desired functionality, useful in AI regulation, legal compliance, safety, and privacy. LLM unlearning methods aim to remove information on demand from LLMs. Current LLM unlearning methods struggle to balance the unlearning efficacy and utility due to the competing nature of these objectives. Keeping the unlearning process computationally feasible without assuming access to the model weights is an overlooked area. In this work we show that \textit{agents might be all we need for effective and practical inference-time LLM unlearning}. We present the first agentic LLM unlearning (\texttt{ALU}) method, a multi-agent, retrain-free, model-agnostic approach to LLM unlearning that achieves effective unlearning while preserving the utility. Our \texttt{ALU} framework unlearns by involving multiple LLM agents, each designed for a specific step in the unlearning process, without the need to update model weights for any of the agents in the framework. Users can easily request any set of unlearning instances in any sequence, and \texttt{ALU} seamlessly adapts in real time. This is facilitated without requiring any changes in the underlying LLM model. Through extensive experiments on established benchmarks (TOFU, WMDP, WPU) and jailbreaking techniques (many shot, target masking, other languages), we demonstrate that \texttt{ALU} consistently stands out as the most robust inference-time LLM unlearning framework among current state-of-the-art methods while incurring time cost that remains effectively constant regardless of the number of unlearning targets. We further highlight \texttt{ALU}'s superior performance compared to existing methods when evaluated at scale. Specifically, \texttt{ALU} is assessed on up to 1000 unlearning targets, exceeding the evaluation scope of all previously proposed LLM unlearning methods.

Figures

Figures reproduced from arXiv: 2502.00406 by the authors.

Figure 1
Figure 1. Using LLM agents for fine-grained post hoc unlearning. The query “How was Victor Krum’s experience at the Yule Ball?” is challenging due to indirect references to the unlearning target Hermione Granger in the response. The Vanilla Agent generates an initial, unmodified response. AuditErase detects the target reference in this response and generates k sanitized variations. The Critic evaluates these responses on a 1–… view at source ↗
Figure 2
Figure 2. We observe a significant increase in Retain ROUGE F-Scores on TOFU 10% with [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. A comparative analysis of seven unlearning frameworks across five key criteria [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Number of responses exhibiting information leakage for five different unlearning [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Counting False Positive responses(when the model gatekeeps information for [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]
Figure 6
Figure 6. Figure 6: The formatting template for WMDP and MMLU multiple-choice questions used [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. SoK: Machine Unlearning for Large Language Models

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A new taxonomy for LLM unlearning distinguishes removal-intended from suppression-intended methods, and argues that gradient ascent methods functionally behave like suppression.

Reference graph

Works this paper leans on

50 extracted references · 7 canonical work pages · cited by 1 Pith paper

  1. [1]

    Hewett, Mojan Javaheripi, Piero Kauffmann, James R

    Marah Abdin, Jyoti Aneja, Harkirat Behl, S´ebastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J. Hewett, Mojan Javaheripi, Piero Kauffmann, James R. Lee, Yin Tat Lee, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingli Yu...

    arXiv

  2. [4]

    35 Tom B

    URL https://arxiv.org/abs/2309.16609. 35 Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, et al. Language models are few-shot learners,

    arXiv

  3. [5]

    2 Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, et al

    URL https://arxiv.org/abs/2005.14165. 2 Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, et al. Internlm2 technical report,

    arXiv 2005

  4. [7]

    3 10 Minseok Choi, Daniel Rim, Dohyun Lee, and Jaegul Choo

    URL https://arxiv.org/abs/2308.07201. 3 10 Minseok Choi, Daniel Rim, Dohyun Lee, and Jaegul Choo. Opt-out: Investigating entity- level unlearning for large language models via optimal transport,

    arXiv

  5. [8]

    6, 24 Vikram S

    URL https: //arxiv.org/abs/2406.12329. 6, 24 Vikram S. Chundawat, Ayush K. Tarun, Murari Mandal, and Mohan Kankanhalli. Zero-shot machine unlearning. IEEE T ransactions on Information Forensics and Security, 18:2345–2354, 2023a. ISSN 1556-6021. doi: 10.1109/tifs.2023.3265506. URL http://dx.doi.org/10.1109/ TIFS.2023.3265506. 1 Vikram S Chundawat, Ayush K ...

    arXiv

  6. [9]

    2, 15 Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu

    URL https://arxiv.org/abs/2310.02238. 2, 15 Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu. Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation,

    arXiv

  7. [10]

    6, 7, 24 Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, and Daniel Kang

    URL https://arxiv.org/abs/2310.12508. 6, 7, 24 Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, and Daniel Kang. Llm agents can autonomously hack websites,

    arXiv

  8. [11]

    1 Jack Foster, Stefan Schoepf, and Alexandra Brintrup

    URL https://arxiv.org/abs/2402.06664. 1 Jack Foster, Stefan Schoepf, and Alexandra Brintrup. Fast machine unlearning without retraining through selective synaptic dampening,

    arXiv

Show all 50 references
  1. [12]

    7, 25 Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, et al

    URL https://arxiv.org/abs/ 2308.07707. 7, 25 Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, et al. The llama 3 herd of models,

    arXiv

  2. [13]

    1, 6, 8, 15, 17, 18, 35 Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang, and Nanyun Peng

    URL https://arxiv.org/abs/2407.21783. 1, 6, 8, 15, 17, 18, 35 Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang, and Nanyun Peng. Model editing harms general abilities of large language models: Regular- ization to the rescue,

    arXiv

  3. [14]

    1 Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, et al

    URL https://arxiv.org/abs/2401.04700. 1 Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, et al. Deepseek-coder: When the large language model meets programming – the rise of code intelligence,

    arXiv

  4. [15]

    6, 35 Bahareh Harandizadeh, Abel Salinas, and Fred Morstatter

    URL https: //arxiv.org/abs/2401.14196. 6, 35 Bahareh Harandizadeh, Abel Salinas, and Fred Morstatter. Risk and response in large language models: Evaluating key threat categories,

    arXiv

  5. [16]

    1 Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A

    URL https://arxiv.org/abs/ 2403.14988. 1 Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, and Percy Liang. Foundation models and fair use,

    arXiv

  6. [18]

    19 Daphne Ippolito, Florian Tram`er, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Kather- ine Lee, Christopher A

    URL https://arxiv.org/abs/2106.09685. 19 Daphne Ippolito, Florian Tram`er, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Kather- ine Lee, Christopher A. Choquette-Choo, and Nicholas Carlini. Preventing verba- tim memorization in language models gives a false sense of privacy,

    arXiv

  7. [19]

    1 Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo

    URL https://arxiv.org/abs/2210.17546. 1 Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. Knowledge unlearning for mitigating privacy risks in language models. arXiv preprint arXiv:2210.01504,

    arXiv

  8. [21]

    1 Meghdad Kurmanji, Eleni Triantafillou, and Peter Triantafillou

    URL https://arxiv.org/abs/2310.13771. 1 Meghdad Kurmanji, Eleni Triantafillou, and Peter Triantafillou. Machine unlearning in learned databases: An experimental analysis, 2023a. URL https://arxiv.org/abs/2311. 17276. 7, 24 Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, an...

    arXiv

  9. [22]

    3 Indian Legislative

    URL https://arxiv.org/abs/2411.00409. 3 Indian Legislative. Digital personal data protection act, 2023,

    2023 arXiv

  10. [23]

    1, 2, 6, 7, 8, 18, 19, 25 Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe

    URL https://arxiv.org/ abs/2403.03218. 1, 2, 6, 7, 8, 18, 19, 25 Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step,

    arXiv

  11. [24]

    23 Chin-Yew Lin

    URL https://arxiv.org/abs/2305.20050. 23 Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In T ext Summa- rization Branches Out, pages 74–81, Barcelona, Spain, July

    arXiv

  12. [26]

    Tofu: A task of fictitious unlearning for llms

    2, 3, 4, 8, 21 Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C Lipton, and J Zico Kolter. Tofu: A task of fictitious unlearning for llms. arXiv preprint arXiv:2401.06121,

    arXiv

  13. [27]

    3, 18 Michael McCloskey and Neal J

    URL https://arxiv.org/abs/2402.15911. 3, 18 Michael McCloskey and Neal J. Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. volume 24 ofPsychology of Learning and Motivation, pages 109–165. Academic Press,

    arXiv

  14. [29]

    3 12 Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi

    URL https://arxiv.org/abs/2402.00751. 3 12 Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi. Descent-to-delete: Gradient-based methods for machine unlearning,

  15. [30]

    2 California Department of Justice OAG

    URL https://arxiv.org/abs/2007.02923. 2 California Department of Justice OAG. Ccpa regulations: Final regulation text,

    2007 arXiv

  16. [31]

    com/index/new-embedding-models-and-api-updates

    URL https://openai. com/index/new-embedding-models-and-api-updates . 18 Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju. In-context unlearning: Language models as few shot unlearners. arXiv preprint arXiv:2310.07579,

    arXiv

  17. [32]

    24 Abhinav Rao, Sachin Vashistha, Atharva Naik, Somak Aditya, and Monojit Choudhury

    URL https://arxiv.org/abs/2305.18290. 24 Abhinav Rao, Sachin Vashistha, Atharva Naik, Somak Aditya, and Monojit Choudhury. Tricking llms into disobedience: Formalizing, analyzing, and detecting jailbreaks,

    arXiv

  18. [33]

    3 Paul-Edouard Sarlin, Eduard Trulls, Marc Pollefeys, Jan Hosang, and Simon Lynen

    URL https://arxiv.org/abs/2305.14965. 3 Paul-Edouard Sarlin, Eduard Trulls, Marc Pollefeys, Jan Hosang, and Simon Lynen. Snap: Self-supervised neural maps for visual positioning and semantic understanding,

    arXiv

  19. [34]

    7, 24 Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, and Stephan Gunnemann

    URL https://arxiv.org/abs/2306.05407. 7, 24 Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, and Stephan Gunnemann. Soft prompt threats: Attacking safety alignment and unlearning in open-source llms through the embedding space. arXiv preprint arXiv:2402.09063,

    arXiv

  20. [35]

    8 Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang

    URL https://arxiv.org/abs/2311.03348. 8 Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. ”do anything now”: Characterizing and evaluating in-the-wild jailbreak prompts on large language models,

    arXiv

  21. [36]

    8 Yash Sinha, Murari Mandal, and Mohan Kankanhalli

    URL https://arxiv.org/abs/2308.03825. 8 Yash Sinha, Murari Mandal, and Mohan Kankanhalli. Unstar: Unlearning with self-taught anti-sample reasoning for llms,

    arXiv

  22. [37]

    3, 6, 8, 15, 19, 24 Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar

    URL https://arxiv.org/abs/2410.17050. 3, 6, 8, 15, 19, 24 Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling llm test-time compute optimally can be more effective than scaling model parameters,

    arXiv

  23. [38]

    18, 23 Robin Staab, Mark Vero, Mislav Balunovi ´c, and Martin Vechev

    URL https:// arxiv.org/abs/2408.03314. 18, 23 Robin Staab, Mark Vero, Mislav Balunovi ´c, and Martin Vechev. Beyond memorization: Violating privacy via inference with large language models,

    arXiv

  24. [39]

    org/abs/2310.07298

    URL https://arxiv. org/abs/2310.07298. 1 Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, and Mohan Kankanhalli. Fast yet effective machine unlearning. IEEE T ransactions on Neural Networks and Learning Systems, 35(9):13046–13055, September

    arXiv

  25. [40]

    doi: 10.1109/tnnls.2023.3266233

    ISSN 2162-2388. doi: 10.1109/tnnls.2023.3266233. URL http://dx.doi.org/10.1109/TNNLS.2023.3266233. 1 13 Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, Soroosh Mariooryad, Yifan Ding, Xi...

    2023

  26. [41]

    1, 6, 7, 8, 15, 17, 18, 35 Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith

    URL https: //qwenlm.github.io/blog/qwen2.5/. 1, 6, 7, 8, 15, 17, 18, 35 Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith. Guardrail baselines for unlearning in llms. arXiv preprint arXiv:2403.03329,

    arXiv

  27. [42]

    6, 35 European Union

    URL https://arxiv.org/abs/2307.09288. 6, 35 European Union. General data protection regulation (gdpr),

    arXiv

  28. [44]

    15 Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, and Hongzhi Yin

    Accessed: 2025-01-13. 15 Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, and Hongzhi Yin. Kga: A general machine unlearning framework based on knowledge gap alignment, 2023a. URL https://arxiv.org/abs/2305.06535. 2 Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc ...

    2025 arXiv

  29. [45]

    16, 18 Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, and Kamalika Chaudhuri

    URL https://arxiv.org/abs/2201.11903. 16, 18 Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, and Kamalika Chaudhuri. Evaluating deep unlearning in large language models. arXiv preprint arXiv:2410.15153,

    arXiv

  30. [46]

    doi: 10.1109/tetci.2024.3379240

    ISSN 2471-285X. doi: 10.1109/tetci.2024.3379240. URL http: //dx.doi.org/10.1109/TETCI.2024.3379240. 1 Yuanshun Yao, Xiaojun Xu, and Yang Liu. Large language model unlearning,

    2024

  31. [47]

    3, 6, 7, 23 Lefeng Zhang, Tianqing Zhu, Haibin Zhang, Ping Xiong, and Wanlei Zhou

    URL https://arxiv.org/abs/2310.10683. 3, 6, 7, 23 Lefeng Zhang, Tianqing Zhu, Haibin Zhang, Ping Xiong, and Wanlei Zhou. Fedrecovery: Differentially private machine unlearning for federated learning frameworks. IEEE T ransactions on Information Forensics and Security,

    arXiv

  32. [48]

    true way

    URL https://arxiv.org/abs/2404. 05868. 1, 6, 15, 24 14 Algorithm 1 ALU Require: Q (prompt), T = {t1, t2, . . . ,tn} (unlearning targets), k (variations), j (top responses) 1: Initialize Mv (Vanilla Agent), Ma (AuditErase Agent), Mcr (Critic Agent), Mcp (Composer Agent) 2: Defi...

    2024

  33. [49]

    How was Victor Krum’s Yule Ball experience?

    To evaluate the model utility, we trained Qwen2.5-14B on the college_chemistry subset of MMLU for the 6 optimization-based 19 methods in Table 1, since we used the wmdp-chem subset for that table. A total of ∼ 82 GPU hours were consumed to train all the 31 models on WMDP , and...

    2024

  34. [50]

    negative instructions

    is observed in the formulation below, where β > 0 is the inverse temperature. LDPO,β(θ) =− 1 β EDpaired log σ β log πθ(yw|x) πref(yw|x) − β log πθ(y1|x) πref(y1|x) NPO-KL and NPO-RT and simple extensions of the loss above: LNPO-KL = LNPO + LKL LNPO-RT = LNPO + LFine-tune SNAP ...

    2023

  35. [1989]

    URL https://www.sciencedirect.com/science/article/pii/S0079742108605368

    doi: https://doi.org/10.1016/S0079-7421(08)60536-8. URL https://www.sciencedirect.com/science/article/pii/S0079742108605368. 7 Andrei Muresanu, Anvith Thudi, Michael R. Zhang, and Nicolas Papernot. Unlearnable algorithms for in-context learning,

    doi

  36. [2004]

    URL https://aclanthology.org/W04-1013/

    Association for Computa- tional Linguistics. URL https://aclanthology.org/W04-1013/. 6, 18 Chris Yuhao Liu, Yaxuan Wang, Jeffrey Flanigan, and Yang Liu. Large language model unlearning via embedding-corrupted prompts, 2024a. URL https://arxiv.org/abs/ 2406.07933. 3, 4, 5, 6, 7...

    arXiv

  37. [2016]

    URL https://gdpr-info. eu/. 1 U.S. Census Bureau. Frequently occurring surnames from the 2010 census. https://www2. census.gov/topics/genealogy/2010surnames/surnames.pdf,

    2010

  38. [2020]

    1 Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al

    URL https://arxiv.org/abs/2008.03156. 1 Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al. The falcon series of open language models,

    2008 arXiv

  39. [2021]

    6, 7, 18, 19 Edward J

    URL https://arxiv.org/abs/2009.03300. 6, 7, 18, 19 Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models,

    2009 arXiv

  40. [2022]

    Soul: Unlocking the power of second-order optimization for llm unlearning

    2, 3 11 Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, and Sijia Liu. Soul: Unlocking the power of second-order optimization for llm unlearning. arXiv preprint arXiv:2404.18239,

    arXiv

  41. [2023]

    6, 17, 35 Cem Anil, Esin Durmus, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Nina Rimsky, Meg Tong, Jesse Mu, Daniel Ford, et al

    URL https://arxiv.org/abs/2311.16867. 6, 17, 35 Cem Anil, Esin Durmus, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Nina Rimsky, Meg Tong, Jesse Mu, Daniel Ford, et al. Many-shot jailbreaking. Anthropic, April,

    arXiv

  42. [2024]

    35 Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu

    URL https://arxiv.org/abs/2403.17297. 35 Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. Chateval: Towards better llm-based evaluators through multi-agent debate,

    arXiv

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.