3 citations · 3 across the 4 of their papers we have counts for
6 papers
Acceleration of Diffusion Language Model through Discrete Average Generator
Yidong Ouyang, Zhengyan Wan, Themis Haris +8
Discrete diffusion models and flow matching have emerged as powerful frameworks for generative modeling over discrete state spaces, yet efficient few-step generation remains a fund…
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…
What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language Models
Abdelrahman Abdelhamed, Mahmoud Afifi, Alec Go
Large language models (LLMs) have been effectively used for many computer vision tasks, including image classification. In this paper, we present a simple yet effective approach fo…