title: ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging
summary: This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4:
  Unlearning Sensitive Content from Large Language Models. This task aims to
  selectively erase sensitive knowledge from large language models, avoiding both
  over-forgetting and under-forgetting issues. We propose an unlearning system
  that leverages Model Merging (specifically TIES-Merging), combining two
  specialized models into a more balanced unlearned model. Our system achieves
  competitive results, ranking second among 26 teams, with an online score of
  0.944 for Task Aggregate and 0.487 for overall Aggregate. In this paper, we
  also conduct local experiments and perform a comprehensive analysis of the
  unlearning process, examining performance trajectories, loss dynamics, and
  weight perspectives, along with several supplementary experiments, to
  understand the effectiveness of our method. Furthermore, we analyze the
  shortcomings of our method and evaluation metrics, emphasizing that MIA scores
  and ROUGE-based metrics alone are insufficient to fully evaluate successful
  unlearning. Finally, we emphasize the need for more comprehensive evaluation
  methodologies and rethinking of unlearning objectives in future research. Code
  is available at https://github.com/zjunlp/unlearn/tree/main/semeval25.
paper_authors._id: 67e602b6dabfd9d4bbf10849
paper_authors.name: Haoming Xu
paper_authors.status: extracted_pending
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paper_authors.avatarUrl: /avatars/0f6ded5fd9cf4e6f0b180b5aa329ea33.svg
paper_authors.fullname: Haoming Xu
paper_authors.type: user
paper_authors.user: HaomingXu
paper_authors._id: 67e602b6dabfd9d4bbf1084a
paper_authors.name: Shuxun Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-28T08:37:57.087Z
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paper_authors.fullname: Shuxun Wang
paper_authors.type: user
paper_authors.user: Saberlve
paper_authors._id: 67e602b6dabfd9d4bbf1084b
paper_authors.name: Yanqiu Zhao
paper_authors._id: 67e602b6dabfd9d4bbf1084c
paper_authors.name: Yi Zhong
paper_authors._id: 67e602b6dabfd9d4bbf1084d
paper_authors.name: Ziyan Jiang
paper_authors._id: 67e602b6dabfd9d4bbf1084e
paper_authors.name: Ningyuan Zhao
paper_authors._id: 67e602b6dabfd9d4bbf1084f
paper_authors.name: Shumin Deng
paper_authors._id: 67e602b6dabfd9d4bbf10850
paper_authors.name: Huajun Chen
paper_authors._id: 67e602b6dabfd9d4bbf10851
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T12:27:20.847Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_title: ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging
paper_summary: This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4:
  Unlearning Sensitive Content from Large Language Models. This task aims to
  selectively erase sensitive knowledge from large language models, avoiding both
  over-forgetting and under-forgetting issues. We propose an unlearning system
  that leverages Model Merging (specifically TIES-Merging), combining two
  specialized models into a more balanced unlearned model. Our system achieves
  competitive results, ranking second among 26 teams, with an online score of
  0.944 for Task Aggregate and 0.487 for overall Aggregate. In this paper, we
  also conduct local experiments and perform a comprehensive analysis of the
  unlearning process, examining performance trajectories, loss dynamics, and
  weight perspectives, along with several supplementary experiments, to
  understand the effectiveness of our method. Furthermore, we analyze the
  shortcomings of our method and evaluation metrics, emphasizing that MIA scores
  and ROUGE-based metrics alone are insufficient to fully evaluate successful
  unlearning. Finally, we emphasize the need for more comprehensive evaluation
  methodologies and rethinking of unlearning objectives in future research. Code
  is available at https://github.com/zjunlp/unlearn/tree/main/semeval25.
paper_ai_summary: A team presents an unlearning system using Model Merging to selectively remove sensitive knowledge from large language models, achieving competitive results in SemEval-2025 Task 4.
paper_ai_keywords: Model Merging TIES-Merging unlearning sensitive knowledge over-forgetting under-forgetting performance trajectories loss dynamics weight perspectives MIA scores ROUGE-based metrics unlearning objectives