Publications

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Conference Papers

ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging

Published in SemEval-2025, 2025

This paper presents our approach to SemEval-2025 Task 4, which focuses on unlearning in semantic understanding. We propose a model merging strategy that consolidates alternative model versions to enforce effective knowledge removal.

Recommended citation: Haoming Xu, Shuxun Wang, Yanqiu Zhao, Yi Zhong, Ziyan Jiang, Ningyuan Zhao, Shumin Deng, Huajun Chen, Ningyu Zhang. "ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging." SemEval 2025.
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Relearn: Unlearning via Learning for Large Language Models

Published in arXiv preprint, 2025

This paper introduces Relearn, a novel framework for machine unlearning in large language models that enables effective removal of specific knowledge while preserving model performance through a learning-based approach.

Recommended citation: Haoming Xu, Ningyuan Zhao, Liming Yang, Sendong Zhao, Shumin Deng, Mengru Wang, Bryan Hooi, Nay Oo, Huajun Chen, Ningyu Zhang. "Relearn: Unlearning via Learning for Large Language Models." ACL 2025.
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MLLM Can See? Dynamic Correction Decoding for Hallucination Mitigation

Published in arXiv preprint, 2024

This paper introduces a dynamic correction decoding strategy for multimodal large language models (MLLMs) that leverages visual information to detect and correct hallucinations during text generation, significantly reducing factual errors.

Recommended citation: Chenxi Wang, Xiang Chen, Ningyu Zhang, Bozhong Tian, Haoming Xu, Shumin Deng, Huajun Chen. "MLLM Can See? Dynamic Correction Decoding for Hallucination Mitigation." ICLR 2025.
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