Relearn: Machine Unlearning Framework for LLMs
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.
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portfolio
Dynamic Correction Decoding for Hallucination Mitigation
A decoding strategy for multimodal large language models that leverages visual information to detect and correct hallucinations during text generation, significantly reducing factual errors.
publications
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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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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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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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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