@inproceedings{borisiuk-etal-2026-anatomy,
title = "Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning",
author = "Borisiuk, Anna and
Savchenko, Andrey and
Panchenko, Alexander and
Tutubalina, Elena",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1287/",
pages = "25846--25859",
ISBN = "979-8-89176-395-1",
abstract = "Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and largely ignores whether the forgotten knowledge originates from pretraining or supervised fine-tuning (SFT). In this paper, we introduce DUAL (Dual Unlearning Evaluation across Training Stages), a benchmark of 28.6k Wikidata-derived triplets annotated with fact popularity using Wikipedia link counts and LLM-based salience scores. Our experiments show that pretrained and SFT models respond differently to unlearning. An SFT step on the forget data yields smoother forgetting, more stable tuning, and 10{--}50{\%} higher retention, while direct unlearning on pretrained models remains unstable and prone to relearning or catastrophic forgetting."
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<abstract>Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and largely ignores whether the forgotten knowledge originates from pretraining or supervised fine-tuning (SFT). In this paper, we introduce DUAL (Dual Unlearning Evaluation across Training Stages), a benchmark of 28.6k Wikidata-derived triplets annotated with fact popularity using Wikipedia link counts and LLM-based salience scores. Our experiments show that pretrained and SFT models respond differently to unlearning. An SFT step on the forget data yields smoother forgetting, more stable tuning, and 10–50% higher retention, while direct unlearning on pretrained models remains unstable and prone to relearning or catastrophic forgetting.</abstract>
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%0 Conference Proceedings
%T Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning
%A Borisiuk, Anna
%A Savchenko, Andrey
%A Panchenko, Alexander
%A Tutubalina, Elena
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F borisiuk-etal-2026-anatomy
%X Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and largely ignores whether the forgotten knowledge originates from pretraining or supervised fine-tuning (SFT). In this paper, we introduce DUAL (Dual Unlearning Evaluation across Training Stages), a benchmark of 28.6k Wikidata-derived triplets annotated with fact popularity using Wikipedia link counts and LLM-based salience scores. Our experiments show that pretrained and SFT models respond differently to unlearning. An SFT step on the forget data yields smoother forgetting, more stable tuning, and 10–50% higher retention, while direct unlearning on pretrained models remains unstable and prone to relearning or catastrophic forgetting.
%U https://aclanthology.org/2026.findings-acl.1287/
%P 25846-25859
Markdown (Informal)
[Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning](https://aclanthology.org/2026.findings-acl.1287/) (Borisiuk et al., Findings 2026)
ACL