1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2025
No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
Wenhang Shi, Yiren Chen, Shuqing Bian +6
Prompt engineering is crucial for leveraging the full potential of large language models (LLMs). While automatic prompt optimization offers a scalable alternative to costly manual…
cs.LG2025★ 1 cited
STAR: Stage-Wise Attention-Guided Token Reduction for Efficient Large Vision-Language Models Inference
Yichen Guo, Hanze Li, Zonghao Zhang +3
Although large vision-language models (LVLMs) leverage rich visual token representations to achieve strong performance on multimodal tasks, these tokens also introduce significant…
cs.CV2025
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization
Huiyi Chen, Jiawei Peng, Kaihua Tang +2
In-context learning (ICL) enables Large Vision-Language Models (LVLMs) to adapt to new tasks without parameter updates, using a few demonstrations from a large support set. However…