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September 23, 2025Open Access

iShumei-Chinchunmei at SemEval-2025 Task 4: A balanced forgetting and retention multi-task framework using effective unlearning loss

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The proposed forgetting loss enhances forgetting effectiveness while maintaining model performance.
An evaluation benchmark was established using three unlearning datasets to assess effectiveness and retention.
This framework effectively integrates various techniques to optimize unlearning processes in machine learning.
Our system achieved a notable ranking of 5th on the SemEval 2025 competition leaderboard.

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This method integrates forgetting loss to improve unlearning effectiveness in large language models, highlighting the preservation of capabilities.

Sun et al. (2025) studied this question.

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This method integrates forgetting loss to improve unlearning effectiveness in large language models, highlighting the preservation of capabilities.