@inproceedings{yu-etal-2026-cascade,
title = "Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning",
author = "Yu, Qingchen and
Duan, Shiying and
Li, Xiaodong and
Wang, Yuhua and
li, Zhiyu and
Zhou, Shiji and
Sun, Yifan and
Fan, Zhaoxin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.794/",
pages = "16812--16838",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Model (LLM) unlearning is essential for removing sensitive or copyrighted knowledge while preserving general utility. Existing methods often leave residual knowledge in intermediate representations, which can still be recovered. To address this, we propose \textit{Cascade}, a hierarchical recoverability control framework that minimizes the internal identifiability of target knowledge. Cascade combines three complementary controls: path-level routing to suppress privacy-associated activation routes, representation-level compression to reduce geometric separability, and decoding-level intervention to limit residual recovery. Experiments on TOFU, MUSE-News, and WMDP, including robustness tests with query reformulation and extraction-style prompts, show that Cascade effectively reduces recoverability while maintaining stable model utility."
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<abstract>Large Language Model (LLM) unlearning is essential for removing sensitive or copyrighted knowledge while preserving general utility. Existing methods often leave residual knowledge in intermediate representations, which can still be recovered. To address this, we propose Cascade, a hierarchical recoverability control framework that minimizes the internal identifiability of target knowledge. Cascade combines three complementary controls: path-level routing to suppress privacy-associated activation routes, representation-level compression to reduce geometric separability, and decoding-level intervention to limit residual recovery. Experiments on TOFU, MUSE-News, and WMDP, including robustness tests with query reformulation and extraction-style prompts, show that Cascade effectively reduces recoverability while maintaining stable model utility.</abstract>
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%0 Conference Proceedings
%T Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning
%A Yu, Qingchen
%A Duan, Shiying
%A Li, Xiaodong
%A Wang, Yuhua
%A li, Zhiyu
%A Zhou, Shiji
%A Sun, Yifan
%A Fan, Zhaoxin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yu-etal-2026-cascade
%X Large Language Model (LLM) unlearning is essential for removing sensitive or copyrighted knowledge while preserving general utility. Existing methods often leave residual knowledge in intermediate representations, which can still be recovered. To address this, we propose Cascade, a hierarchical recoverability control framework that minimizes the internal identifiability of target knowledge. Cascade combines three complementary controls: path-level routing to suppress privacy-associated activation routes, representation-level compression to reduce geometric separability, and decoding-level intervention to limit residual recovery. Experiments on TOFU, MUSE-News, and WMDP, including robustness tests with query reformulation and extraction-style prompts, show that Cascade effectively reduces recoverability while maintaining stable model utility.
%U https://aclanthology.org/2026.findings-emnlp.794/
%P 16812-16838
Markdown (Informal)
[Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning](https://aclanthology.org/2026.findings-emnlp.794/) (Yu et al., Findings 2026)
ACL
- Qingchen Yu, Shiying Duan, Xiaodong Li, Yuhua Wang, Zhiyu li, Shiji Zhou, Yifan Sun, and Zhaoxin Fan. 2026. Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 16812–16838, Budapest, Hungary. Association for Computational Linguistics.