Sitemap
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
Pyhon Learning
Published:
python小知识点
Pytorch Learning
Published:
torch函数
Uncertainty-Confidence
Published:
深度学习的不确定性和校准
Position Embedding
Published:
位置编码
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: Xu, H., et al. (2024). "MLLM Can See? Dynamic Correction Decoding for Hallucination Mitigation." arXiv preprint arXiv:2406.00000.
Download Paper
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: Xu, H., et al. (2025). "Relearn: Unlearning via Learning for Large Language Models." arXiv preprint arXiv:2408.15168.
Download Paper
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: Xu, H., et al. (2025). "ZJUKLAB at SemEval-2025 Task 4: Unlearning via Model Merging." SemEval-2025.
Download Paper
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.