@inproceedings{wang-etal-2025-dutir,
title = "{DUT}ir at {S}em{E}val-2025 Task 4: Optimized Fine-Tuning of Linear Layers for Balanced Knowledge Forgetting and Retention",
author = "Wang, Zekun and
Zeng, Jingjie and
Li, Yingxu and
Yang, Liang and
Lin, Hongfei",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.72/",
pages = "522--526",
ISBN = "979-8-89176-273-2",
abstract = "This paper describes our system used in SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. In this work, we propose a method for controlling the fine-tuning of a model{'}s linear layers, referred to as CTL-Finetune (Control-Tuned Linear Fine-tuning). The goal of our method is to allow the model to forget specific information while preserving the knowledge it needs to retain. The method consists of four main components: 1) shuffling data labels, 2) shuffling label gradient calculation, 3) determination of control layers, and 4) fine-tuning using a combination of gradient ascent and gradient descent. Experimental results demonstrate that our approach effectively enables the model to forget targeted knowledge while minimizing the impact on retained information, thus maintaining the model{'}s overall performance."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2025-dutir">
<titleInfo>
<title>DUTir at SemEval-2025 Task 4: Optimized Fine-Tuning of Linear Layers for Balanced Knowledge Forgetting and Retention</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zekun</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jingjie</namePart>
<namePart type="family">Zeng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yingxu</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liang</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hongfei</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Rosenthal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aiala</namePart>
<namePart type="family">Rosá</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcos</namePart>
<namePart type="family">Zampieri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-273-2</identifier>
</relatedItem>
<abstract>This paper describes our system used in SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. In this work, we propose a method for controlling the fine-tuning of a model’s linear layers, referred to as CTL-Finetune (Control-Tuned Linear Fine-tuning). The goal of our method is to allow the model to forget specific information while preserving the knowledge it needs to retain. The method consists of four main components: 1) shuffling data labels, 2) shuffling label gradient calculation, 3) determination of control layers, and 4) fine-tuning using a combination of gradient ascent and gradient descent. Experimental results demonstrate that our approach effectively enables the model to forget targeted knowledge while minimizing the impact on retained information, thus maintaining the model’s overall performance.</abstract>
<identifier type="citekey">wang-etal-2025-dutir</identifier>
<location>
<url>https://aclanthology.org/2025.semeval-1.72/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>522</start>
<end>526</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T DUTir at SemEval-2025 Task 4: Optimized Fine-Tuning of Linear Layers for Balanced Knowledge Forgetting and Retention
%A Wang, Zekun
%A Zeng, Jingjie
%A Li, Yingxu
%A Yang, Liang
%A Lin, Hongfei
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F wang-etal-2025-dutir
%X This paper describes our system used in SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models. In this work, we propose a method for controlling the fine-tuning of a model’s linear layers, referred to as CTL-Finetune (Control-Tuned Linear Fine-tuning). The goal of our method is to allow the model to forget specific information while preserving the knowledge it needs to retain. The method consists of four main components: 1) shuffling data labels, 2) shuffling label gradient calculation, 3) determination of control layers, and 4) fine-tuning using a combination of gradient ascent and gradient descent. Experimental results demonstrate that our approach effectively enables the model to forget targeted knowledge while minimizing the impact on retained information, thus maintaining the model’s overall performance.
%U https://aclanthology.org/2025.semeval-1.72/
%P 522-526
Markdown (Informal)
[DUTir at SemEval-2025 Task 4: Optimized Fine-Tuning of Linear Layers for Balanced Knowledge Forgetting and Retention](https://aclanthology.org/2025.semeval-1.72/) (Wang et al., SemEval 2025)
ACL