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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
超越表层对齐：信念是通往深层对齐的新入口 Permalink
Published:
从大模型内部的信念出发，探讨信念表征、度量、管理与干预，以及从表层对齐迈向深层对齐的研究方向。
portfolio
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.
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
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.