3 citations · 14 across the 12 of their papers we have counts for
12 papers
The Better Angels of Machine Personality: How Personality Relates to LLM Safety
Jie Zhang, Dongrui Liu, Chen Qian +4
Personality psychologists have analyzed the relationship between personality and safety behaviors in human society. Although Large Language Models (LLMs) demonstrate personality tr…
SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model
Yongting Zhang, Lu Chen, Guodong Zheng +10
The emergence of Vision Language Models (VLMs) has brought unprecedented advances in understanding multimodal information. The combination of textual and visual semantics in VLMs i…
Assessment of Multimodal Large Language Models in Alignment with Human Values
Zhelun Shi, Zhipin Wang, Hongxing Fan +7
Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh). However, in…
MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control
Enshen Zhou, Yiran Qin, Zhenfei Yin +5
It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow…
RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents
Zeren Chen, Zhelun Shi, Xiaoya Lu +8
Achieving generalizability in solving out-of-distribution tasks is one of the ultimate goals of learning robotic manipulation. Recent progress of Vision-Language Models (VLMs) has…
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models
Chen Qian, Jie Zhang, Wei Yao +5
Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies concentrate on fully pre-trained LLMs to better understand and improve LLMs' trustworthiness.…
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
Lijun Li, Bowen Dong, Ruohui Wang +5
In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount. To meet this crucial need, we propose \emph{SALAD-Bench}, a safety…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…
PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety
Zaibin Zhang, Yongting Zhang, Lijun Li +6
Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit profound capabilities in collective intelligence. However, the potential misuse of this intelligence f…
MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception
Yiran Qin, Enshen Zhou, Qichang Liu +5
It is a long-lasting goal to design an embodied system that can solve long-horizon open-world tasks in human-like ways. However, existing approaches usually struggle with compound…
ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models
Zhelun Shi, Zhipin Wang, Hongxing Fan +4
Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. However, even though a list of b…
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE
Zeren Chen, Ziqin Wang, Zhen Wang +7
Recent studies have demonstrated Large Language Models (LLMs) can extend their zero-shot generalization capabilities to multimodal learning through instruction tuning. As more moda…