26 citations · 89 across the 49 of their papers we have counts for
10 papers · 1 filter
Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference
Siyuan Wang, Dianyi Wang, Chengxing Zhou +4
Large Vision-Language Models (LVLMs) typically learn visual capacity through visual instruction tuning, involving updates to both a projector and their LLM backbones. Inspired by t…
Symbolic Working Memory Enhances Language Models for Complex Rule Application
Siyuan Wang, Zhongyu Wei, Yejin Choi +1
Large Language Models (LLMs) have shown remarkable reasoning performance but struggle with multi-step deductive reasoning involving a series of rule application steps, especially w…
Identity-Driven Hierarchical Role-Playing Agents
Libo Sun, Siyuan Wang, Xuanjing Huang +1
Utilizing large language models (LLMs) to achieve role-playing has gained great attention recently. The primary implementation methods include leveraging refined prompts and fine-t…
Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
Shengbin Yue, Siyuan Wang, Wei Chen +2
Recent advancements in Large Language Models (LLMs) have led to significant breakthroughs in various natural language processing tasks. However, generating factually consistent res…
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…
From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
Siyuan Wang, Zhuohan Long, Zhihao Fan +1
The rapid development of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has exposed vulnerabilities to various adversarial attacks. This paper provides a…
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…
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation
Siyuan Wang, Zhuohan Long, Zhihao Fan +2
This paper presents a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models (LLMs), aiming for a more accurate assessment of their capab…
Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs
Siyuan Wang, Zhongyu Wei, Yejin Choi +1
Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls shor…
AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator
Zhihao Fan, Jialong Tang, Wei Chen +5
Artificial intelligence has significantly advanced healthcare, particularly through large language models (LLMs) that excel in medical question answering benchmarks. However, their…