arXiv Artificial Intelligence

Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

1 hour ago 11.09.2026 07:00 👁 1 views
Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

Quick summary

arXiv:2609.10439v1 Announce Type: cross Abstract: Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns. Machine unlearning offers a practical alternative to full retraining, but many existing methods apply broad or fixed parameter updates that can degrade utility and remain brittle under deployment changes such as post-training quantization, where forgotten knowledge may partially re-emerge. We propose Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer-level

Key takeaways

  • arXiv:2609.10439v1 Announce Type: cross Abstract: Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns.
  • Machine unlearning offers a practical alternative to full retraining, but many existing methods apply broad or fixed parameter updates that can degrade utility and remain brittle under deployment changes such as post-training quantization, where forgotten knowledge may partially re-emerge.
  • We propose Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer-level

Why it matters

The significance is not only the legal text but how it changes product design. Decisions around “Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs” may reshape data collection, model training, output accountability and market access.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗