3 citations · 3 across the 5 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…
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…
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
Wei-Lin Chen, Liqian Peng, Tian Tan +5
Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings suggest that…
Compressing Many-Shots in In-Context Learning
Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta +9
Large Language Models (LLMs) have been shown to be able to learn different tasks without explicit finetuning when given many input-output examples / demonstrations through In-Conte…
You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models
Wenchong He, Liqian Peng, Zhe Jiang +1
Large language models (LLMs) possess a remarkable ability to perform in-context learning (ICL), which enables them to handle multiple downstream tasks simultaneously without requir…
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…