9 citations · 24 across the 7 of their papers we have counts for
9 papers
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…
Boosting LLM via Learning from Data Iteratively and Selectively
Qi Jia, Siyu Ren, Ziheng Qin +3
Datasets nowadays are generally constructed from multiple sources and using different synthetic techniques, making data de-noising and de-duplication crucial before being used for…
MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures
Jinjie Ni, Yifan Song, Deepanway Ghosal +10
Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their de…
MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures
Jinjie Ni, Fuzhao Xue, Xiang Yue +5
Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while LLM-…
OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
Fuzhao Xue, Zian Zheng, Yao Fu +4
To help the open-source community have a better understanding of Mixture-of-Experts (MoE) based large language models (LLMs), we train and release OpenMoE, a series of fully open-s…
Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation
Wangbo Zhao, Kai Wang, Xiangxiang Chu +3
Text-based video segmentation aims to segment the target object in a video based on a describing sentence. Incorporating motion information from optical flow maps with appearance a…