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    <abstract>The {textit{Unlearning Sensitive Content from Large Language Models}} task aims to remove targeted datapoints from trained models while minimally affecting their general knowledge. In our work, we leverage parameter-efficient, gradient-based unlearning using low-rank (LoRA) adaptation and layer-focused fine-tuning. To further enhance unlearning effectiveness, we employ data chunking, splitting forget data into disjoint partitions and merging them with cyclically sampled retain samples at a pre-defined ratio. Our task-agnostic method achieves an outstanding forget-retain balance, ranking first on leaderboards and significantly outperforming baselines and competing systems.</abstract>
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%0 Conference Proceedings
%T AILS-NTUA at SemEval-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunking
%A Premptis, Iraklis
%A Lymperaiou, Maria
%A Filandrianos, George
%A Menis Mastromichalakis, Orfeas
%A Voulodimos, Athanasios
%A Stamou, Giorgos
%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 premptis-etal-2025-ails
%X The {textit{Unlearning Sensitive Content from Large Language Models}} task aims to remove targeted datapoints from trained models while minimally affecting their general knowledge. In our work, we leverage parameter-efficient, gradient-based unlearning using low-rank (LoRA) adaptation and layer-focused fine-tuning. To further enhance unlearning effectiveness, we employ data chunking, splitting forget data into disjoint partitions and merging them with cyclically sampled retain samples at a pre-defined ratio. Our task-agnostic method achieves an outstanding forget-retain balance, ranking first on leaderboards and significantly outperforming baselines and competing systems.
%U https://aclanthology.org/2025.semeval-1.184/
%P 1383-1405

Markdown (Informal)

[AILS-NTUA at SemEval-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunking](https://aclanthology.org/2025.semeval-1.184/) (Premptis et al., SemEval 2025)

• AILS-NTUA at SemEval-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunking (Premptis et al., SemEval 2025)

ACL

• Iraklis Premptis, Maria Lymperaiou, George Filandrianos, Orfeas Menis Mastromichalakis, Athanasios Voulodimos, and Giorgos Stamou. 2025. AILS-NTUA at SemEval-2025 Task 4: Parameter-Efficient Unlearning for Large Language Models using Data Chunking. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1383–1405, Vienna, Austria. Association for Computational Linguistics.