3 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
MILO: Efficient Many-shot In-Context Learning with Block-wise Low-rank Compression
Youpeng Zhao, Tian Tan, Liqian Peng +2
Many-shot in-context learning (ICL) enables large language models (LLMs) to adapt to complex tasks by conditioning on thousands of demonstration examples, but this paradigm shifts…
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning
Kai Zhang, Congchao Wang, Liqian Peng +2
Large Language Models (LLMs) have gained significant attention in on-device applications due to their remarkable performance across real-world tasks. However, on-device LLMs often…
Cascade-Aware Training of Language Models
Congchao Wang, Sean Augenstein, Keith Rush +5
Reducing serving cost and latency is a fundamental concern for the deployment of language models (LMs) in business applications. To address this, cascades of LMs offer an effective…