20 citations · 82 across the 18 of their papers we have counts for
7 papers · 1 filter
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: 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…
Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline
Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue +3
Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. However, the inference process for LLMs comes with sign…
Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
Xiao Liu, Jian Zhang, Heng Zhang +2
Compared with standard text, understanding dialogue is more challenging for machines as the dynamic and unexpected semantic changes in each turn. To model such inconsistent semanti…
Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularization
Andrew Koh, Fuzhao Xue, Eng Siong Chng
In this paper, we examine the use of Transfer Learning using Pretrained Audio Neural Networks (PANNs), and propose an architecture that is able to better leverage the acoustic feat…