26 citations · 26 across the 6 of their papers we have counts for
6 papers
OViP: Online Vision-Language Preference Learning for VLM Hallucination
Shujun Liu, Siyuan Wang, Zejun Li +3
Large vision-language models (LVLMs) remain vulnerable to hallucination, often generating content misaligned with visual inputs. Although recent training-based approaches aim to mi…
How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation
Zhuohang Long, Siyuan Wang, Shujun Liu +3
Jailbreak attacks, where harmful prompts bypass generative models' built-in safety, raise serious concerns about model vulnerability. While many defense methods have been proposed,…
Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction
Shengbin Yue, Ting Huang, Zheng Jia +5
Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper i…
HAF-RM: A Hybrid Alignment Framework for Reward Model Training
Shujun Liu, Xiaoyu Shen, Yuhang Lai +5
The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing re…
ALaRM: Align Language Models via Hierarchical Rewards Modeling
Yuhang Lai, Siyuan Wang, Shujun Liu +2
We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF), which is designed to enhance the alignment of large lang…
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
Shengbin Yue, Wei Chen, Siyuan Wang +8
We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies…