3 citations · 13 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…