ClaimcausalA claim that one thing brings about another, not merely that the two go together.constitution →importance · notableImportance 0.60, from 0 to 1 · notable: a contested point in a live debate (also the default before judging). Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

TopicsMachine Unlearning Large language models Latent Knowledge in Language Models

Common LLM unlearning methods suppress targeted information rather than erase it from the model.

Not yet assessed. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.

Decomposition

This claim has not been assessed yet. Attention goes where its expected value is highest and someone funds it; once this claim's assessment is funded and runs, it may well decompose into subclaims.

or create a grant for this whole area →

Provenance

Where this claim has been said, linked to its canonical form.

3 sources · no origin · 3 untraced

3 Untraced

Catastrophic Failure of LLM Unlearning via QuantizationOct 21, 2024 · poses it as a question Untraced · nothing upstream recorded

Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMsMay 22, 2025 Untraced · nothing upstream recorded

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign RelearningJun 19, 2024 Untraced · nothing upstream recorded

originuntraced

Our work indicates that current approximate unlearning methods simply suppress the model outputs and fail to robustly forget target knowledge in the LLMs.

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearningextraction 0.80

Abstract's concluding interpretation of the relearning results.

little attention has been given to whether existing unlearning methods for LLMs truly achieve forgetting or merely hide the knowledge, which current unlearning benchmarks fail to detect.

Catastrophic Failure of LLM Unlearning via Quantizationposes it as a questionextraction 0.60

Abstract; framed as the question the quantization finding answers.

This \emph{reversibility} suggests that information is merely suppressed, not genuinely erased.

Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMsextraction 0.80

Abstract; follows the finding that forgotten behavior is restored by minimal fine-tuning.

Cite this claim: a formal citation with its evidence attached

Embed this claim: show it on another site with its current assessment

An embed reports the graph's judgment as it stands when the page is viewed: the status with its confidence, how disputed the claim is, and the date. It links back here.

CardFits its column and sizes itself. Without the script it stays a quotation with its link.

<blockquote class="minerval-claim" data-claim-id="11af9449-de80-43b7-872c-cad4c1af0c50"><a href="https://minerval.ai/claims/11af9449-de80-43b7-872c-cad4c1af0c50">Common LLM unlearning methods suppress targeted information rather than erase it from the model.</a></blockquote>
<script async src="https://minerval.ai/embed.js"></script>

IframeWhere scripts are not allowed. A fixed height; add ?theme=light or ?theme=dark to fix its colours.

<iframe src="https://minerval.ai/embed/claims/11af9449-de80-43b7-872c-cad4c1af0c50" title="Minerval: Common LLM unlearning methods suppress targeted information rather than erase it from the…" width="480" height="200" style="border:0;max-width:100%" loading="lazy"></iframe>

BadgeAn image for a README or a forum post, in Markdown.

[![Minerval's judgment of this claim](https://minerval.ai/embed/claims/11af9449-de80-43b7-872c-cad4c1af0c50/badge.svg)](https://minerval.ai/claims/11af9449-de80-43b7-872c-cad4c1af0c50)

The badge now:

Contribute

Every judgment on this page is open to challenge. A contribution is evaluated on its merits by the reviewer; if it succeeds the page changes, and if it does not, the reasons are stated. Either way the exchange becomes part of the claim’s public record.

Created by extractor · Nov 11, 2026. Every judgment on this page is accompanied by a reasoning trace.