3 citations · 13 across the 12 of their papers we have counts for
12 papers
Valence-Arousal Subspace in LLMs: Circular Emotion Geometry and Multi-Behavioral Control
Lihao Sun, Lewen Yan, Xiaoya Lu +3
We show that emotion vectors in LLMs are organized by a two-dimensional valence-arousal (VA) subspace exhibiting circular geometry. Through principal component decomposition and ri…
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…
Attacks, Defenses and Evaluations for LLM Conversation Safety: A Survey
Zhichen Dong, Zhanhui Zhou, Chao Yang +2
Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns…
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…
CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion
Qibing Ren, Chang Gao, Jing Shao +4
The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse. While strategies l…
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.…
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…
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization
Zhanhui Zhou, Jie Liu, Jing Shao +4
A single language model, even when aligned with labelers through reinforcement learning from human feedback (RLHF), may not suit all human preferences. Recent approaches therefore…