TULIP: Targeted LLM Unlearning at Layers Identified Per-input
Representation-level unlearning modifies the intermediate hidden states of LLMs. Although knowledge is distributed across layers, existing methods operate at a single fixed layer for the entire forget set. We ask whether such a fixed layer is sufficient for unlearning. To answer this, we design a hijacking experiment that grafts hidden states of the target model into an oracle model. The oracle cannot produce the forget answer on its own, yet it produces the answer from the grafted state. This indicates the answer is already formed at an intermediate layer and read out afterward. Moreover, the layer where formation ends varies widely across inputs. Motivated by these findings, we propose ...
| Authors | Yejin Kim, William F. Shen, Seokwon Jung et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 September 2026 |
| Last updated at source | 14 October 2026 |
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