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      <title>Forgetting by Design: Turning GDPR into a Systems Problem for LLMs</title>
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&lt;h2 id=&#34;tldr-for-operators&#34;&gt;TL;DR for operators&lt;/h2&gt;
&lt;p&gt;A deletion request is not a prompt. It is not a “please forget” instruction, a fine-tuning vibe, or a compliance-flavoured model apology.&lt;/p&gt;
&lt;p&gt;The useful idea in &lt;em&gt;Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models&lt;/em&gt; is much less mystical: make training reproducible enough that deletion can be executed like systems recovery.&lt;sup id=&#34;fnref:1&#34;&gt;&lt;a href=&#34;#fn:1&#34; class=&#34;footnote-ref&#34; role=&#34;doc-noteref&#34;&gt;1&lt;/a&gt;&lt;/sup&gt; The paper treats training as a deterministic program, logs the minimal control inputs needed to replay that program, and then removes the requested data during replay. Under strict preconditions, the resulting parameters are bit-identical, in the training dtype, to the model that would have been produced if the forgotten examples had never been included.&lt;/p&gt;</description>
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