6 papers · 1 filter
Sparser Block-Sparse Attention via Token Permutation
Xinghao Wang, Pengyu Wang, Dong Zhang +7
Scaling the context length of large language models (LLMs) offers significant benefits but is computationally expensive. This expense stems primarily from the self-attention mechan…
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets
Pengyu Wang, Shaojun Zhou, Chenkun Tan +7
Unified vision large language models (VLLMs) have recently achieved impressive advancements in both multimodal understanding and generation, powering applications such as visual qu…
Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLM
Chenkun Tan, Pengyu Wang, Shaojun Zhou +6
Multimodal large language models (MLLMs) have gained significant attention due to their impressive ability to integrate vision and language modalities. Recent advancements in MLLMs…
LongSafety: Enhance Safety for Long-Context LLMs
Mianqiu Huang, Xiaoran Liu, Shaojun Zhou +11
Recent advancements in model architectures and length extrapolation techniques have significantly extended the context length of large language models (LLMs), paving the way for th…
MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
Mozhi Zhang, Pengyu Wang, Chenkun Tan +4
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications. To make LLMs mor…
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
Pengyu Wang, Dong Zhang, Linyang Li +5
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the…