@inproceedings{bhavankar-etal-2025-neuroreset,
title = "{N}euro{R}eset : {LLM} Unlearning via Dual Phase Mixed Methodology",
author = "Bhavankar, Dhwani and
Sevalia, Het and
Agarwal, Shubh and
Kulkarni, Yogesh and
Walambe, Rahee and
Kotecha, Ketan",
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-1.138/",
pages = "1046--1051",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents the method for the unlearning of sensitive information from large language models as applied in the SemEval 2025 Task 4 challenge. The unlearning pipeline consists of two phases. In phase I, the model is instructed to forget specific datasets, and in phase II, the model is stabilized using a retention dataset. Unlearning with these methods secured a final score of 0.420 with the 2nd honorary mention in the 7B parameter challenge and a score of 0.36 in the 13th position for the 1B parameter challenge. The paper presents a background study, a brief literature review, and a gap analysis, as well as the methodology employed in our work titled NeuroReset. The training methodology and evaluation metrics are also presented, and the trade-offs between unlearning efficiency and model performance are discussed. The contributions of the paper are systematic unlearning, a comparative analysis of unlearning methods, and an empirical analysis of model performance post-unlearning."
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%0 Conference Proceedings
%T NeuroReset : LLM Unlearning via Dual Phase Mixed Methodology
%A Bhavankar, Dhwani
%A Sevalia, Het
%A Agarwal, Shubh
%A Kulkarni, Yogesh
%A Walambe, Rahee
%A Kotecha, Ketan
%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 bhavankar-etal-2025-neuroreset
%X This paper presents the method for the unlearning of sensitive information from large language models as applied in the SemEval 2025 Task 4 challenge. The unlearning pipeline consists of two phases. In phase I, the model is instructed to forget specific datasets, and in phase II, the model is stabilized using a retention dataset. Unlearning with these methods secured a final score of 0.420 with the 2nd honorary mention in the 7B parameter challenge and a score of 0.36 in the 13th position for the 1B parameter challenge. The paper presents a background study, a brief literature review, and a gap analysis, as well as the methodology employed in our work titled NeuroReset. The training methodology and evaluation metrics are also presented, and the trade-offs between unlearning efficiency and model performance are discussed. The contributions of the paper are systematic unlearning, a comparative analysis of unlearning methods, and an empirical analysis of model performance post-unlearning.
%U https://aclanthology.org/2025.semeval-1.138/
%P 1046-1051
Markdown (Informal)
[NeuroReset : LLM Unlearning via Dual Phase Mixed Methodology](https://aclanthology.org/2025.semeval-1.138/) (Bhavankar et al., SemEval 2025)
ACL
- Dhwani Bhavankar, Het Sevalia, Shubh Agarwal, Yogesh Kulkarni, Rahee Walambe, and Ketan Kotecha. 2025. NeuroReset : LLM Unlearning via Dual Phase Mixed Methodology. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1046–1051, Vienna, Austria. Association for Computational Linguistics.