@inproceedings{dahal-etal-2026-gone,
title = "{GONE}: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping",
author = "Dahal, Chahana and
Balasubramaniam, Ashutosh and
Xiong, Zuobin",
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.106/",
pages = "2173--2193",
ISBN = "979-8-89176-462-0",
abstract = "Unlearning knowledge is a pressing and challenging task in LLMs because of their unprecedented capability to memorize and digest training data at scale, raising more significant issues on safety, privacy, and intellectual property. However, existing works, including parameter editing, fine-tuning, and distillation-based methods, are all focused on the flat sentence-level data but overlook the relational, multi-hop, and reasoned knowledge in the naturally structured data. In response to this gap, this paper introduces Graph Oblivion and Node Erasure (\textbf{GONE}), a benchmark for evaluating knowledge unlearning over structured knowledge graphs (KGs) facts in LLMs. This KG-based benchmark enables the disentanglement of three effects of unlearning: direct fact removal, reasoning-based leakage, and catastrophic forgetting. In addition, Neighborhood-Expanded Distribution Shaping (\textbf{NEDS}), a novel unlearning framework, is designed to leverage graph connectivity and identify anchor correlated neighbors, enforcing a precise decision boundary between the forgotten fact and its semantic neighborhood. Evaluations on LLaMA-3-8B and Mistral-7B across multiple knowledge editing and unlearning methods showcase the superior performance (1.000 on unlearning efficacy and 0.839 on locality) of NEDS on GONE and other benchmarks."
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<abstract>Unlearning knowledge is a pressing and challenging task in LLMs because of their unprecedented capability to memorize and digest training data at scale, raising more significant issues on safety, privacy, and intellectual property. However, existing works, including parameter editing, fine-tuning, and distillation-based methods, are all focused on the flat sentence-level data but overlook the relational, multi-hop, and reasoned knowledge in the naturally structured data. In response to this gap, this paper introduces Graph Oblivion and Node Erasure (GONE), a benchmark for evaluating knowledge unlearning over structured knowledge graphs (KGs) facts in LLMs. This KG-based benchmark enables the disentanglement of three effects of unlearning: direct fact removal, reasoning-based leakage, and catastrophic forgetting. In addition, Neighborhood-Expanded Distribution Shaping (NEDS), a novel unlearning framework, is designed to leverage graph connectivity and identify anchor correlated neighbors, enforcing a precise decision boundary between the forgotten fact and its semantic neighborhood. Evaluations on LLaMA-3-8B and Mistral-7B across multiple knowledge editing and unlearning methods showcase the superior performance (1.000 on unlearning efficacy and 0.839 on locality) of NEDS on GONE and other benchmarks.</abstract>
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%0 Conference Proceedings
%T GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping
%A Dahal, Chahana
%A Balasubramaniam, Ashutosh
%A Xiong, Zuobin
%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 dahal-etal-2026-gone
%X Unlearning knowledge is a pressing and challenging task in LLMs because of their unprecedented capability to memorize and digest training data at scale, raising more significant issues on safety, privacy, and intellectual property. However, existing works, including parameter editing, fine-tuning, and distillation-based methods, are all focused on the flat sentence-level data but overlook the relational, multi-hop, and reasoned knowledge in the naturally structured data. In response to this gap, this paper introduces Graph Oblivion and Node Erasure (GONE), a benchmark for evaluating knowledge unlearning over structured knowledge graphs (KGs) facts in LLMs. This KG-based benchmark enables the disentanglement of three effects of unlearning: direct fact removal, reasoning-based leakage, and catastrophic forgetting. In addition, Neighborhood-Expanded Distribution Shaping (NEDS), a novel unlearning framework, is designed to leverage graph connectivity and identify anchor correlated neighbors, enforcing a precise decision boundary between the forgotten fact and its semantic neighborhood. Evaluations on LLaMA-3-8B and Mistral-7B across multiple knowledge editing and unlearning methods showcase the superior performance (1.000 on unlearning efficacy and 0.839 on locality) of NEDS on GONE and other benchmarks.
%U https://aclanthology.org/2026.findings-emnlp.106/
%P 2173-2193
Markdown (Informal)
[GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping](https://aclanthology.org/2026.findings-emnlp.106/) (Dahal et al., Findings 2026)
ACL