2 citations · 2 across the 8 of their papers we have counts for
8 papers
Subspace Alignment for Vision-Language Model Test-time Adaptation
Zhichen Zeng, Wenxuan Bao, Xiao Lin +8
Vision-language models (VLMs), despite their extraordinary zero-shot capabilities, are vulnerable to distribution shifts. Test-time adaptation (TTA) emerges as a predominant strate…
Towards Effective Model Editing for LLM Personalization
Baixiang Huang, Limeng Cui, Jiapeng Liu +7
Personalization is becoming indispensable for LLMs to align with individual user preferences and needs. Yet current approaches are often computationally expensive, data-intensive,…
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference
Yuxuan Song, Zheng Zhang, Cheng Luo +19
We present Seed Diffusion Preview, a large-scale language model based on discrete-state diffusion, offering remarkably fast inference speed. Thanks to non-sequential, parallel gene…
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts
Leiyu Pan, Zhenpeng Su, Minxuan Lv +10
Large language models have demonstrated exceptional performance across a wide range of tasks. However, dense models usually suffer from sparse activation, where many activation val…
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs
Minxuan Lv, Zhenpeng Su, Leiyu Pan +10
As large language models continue to scale, computational costs and resource consumption have emerged as significant challenges. While existing sparsification methods like pruning…
Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis
Siyuan Yan, Cheng Luo, Zhen Yu +1
Vision-language foundation models like CLIP have shown impressive zero-shot generalization, but finetuning on downstream datasets can cause overfitting and loss of its generalizati…
IEKM: A Model Incorporating External Keyword Matrices
Cheng Luo, Qin Li, Zhao Yan +2
A customer service platform system with a core text semantic similarity (STS) task faces two urgent challenges: Firstly, one platform system needs to adapt to different domains of…
RTP: Rethinking Tensor Parallelism with Memory Deduplication
Cheng Luo, Tianle Zhong, Geoffrey Fox
In the evolving landscape of neural network models, one prominent challenge stand out: the significant memory overheads associated with training expansive models. Addressing this c…