EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models

1 code implementation • 21 Apr 2025 • Ziwen Xu, Shuxun Wang, Kewei Xu, Haoming Xu, Mengru Wang, Xinle Deng, Yunzhi Yao, Guozhou Zheng, Huajun Chen, Ningyu Zhang

In addition, we provide a demo video at https://zjunlp. github. io/project/EasyEdit2/video for a quick introduction.

Language Modeling Language Modelling +1

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

1 code implementation • 27 Mar 2025 • Haoming Xu, Shuxun Wang, Yanqiu Zhao, Yi Zhong, Ziyan Jiang, Ningyuan Zhao, Shumin Deng, Huajun Chen, Ningyu Zhang

This paper presents the ZJUKLAB team's submission for SemEval-2025 Task 4: Unlearning Sensitive Content from Large Language Models.

ReLearn: Unlearning via Learning for Large Language Models

1 code implementation • 16 Feb 2025 • Haoming Xu, Ningyuan Zhao, Liming Yang, Sendong Zhao, Shumin Deng, Mengru Wang, Bryan Hooi, Nay Oo, Huajun Chen, Ningyu Zhang

Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities.

Data Augmentation Text Generation

MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

1 code implementation • 15 Oct 2024 • Chenxi Wang, Xiang Chen, Ningyu Zhang, Bozhong Tian, Haoming Xu, Shumin Deng, Huajun Chen

Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood.

Hallucination Language Modeling +1

Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese

1 code implementation • 8 Sep 2023 • Haochun Wang, Sendong Zhao, Zewen Qiang, Zijian Li, Nuwa Xi, Yanrui Du, MuZhen Cai, Haoqiang Guo, Yuhan Chen, Haoming Xu, Bing Qin, Ting Liu

To address this challenge, we propose knowledge-tuning, which leverages structured medical knowledge bases for the LLMs to grasp domain knowledge efficiently and facilitate reliable response generation.

Domain Adaptation Hallucination +2

Dense Regression Network for Video Grounding

1 code implementation • CVPR 2020 • Runhao Zeng, Haoming Xu, Wenbing Huang, Peihao Chen, Mingkui Tan, Chuang Gan

The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy.

Natural Language Moment Retrieval Natural Language Queries +2