71 citations · 127 across the 9 of their papers we have counts for
10 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…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting
Yufei Li, John Nham, Ganesh Jawahar +7
Generic text rewriting is a prevalent large language model (LLM) application that covers diverse real-world tasks, such as style transfer, fact correction, and email editing. These…
LLM Performance Predictors are good initializers for Architecture Search
Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan +1
In this work, we utilize Large Language Models (LLMs) for a novel use case: constructing Performance Predictors (PP) that estimate the performance of specific deep neural network a…
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar +3
Recent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number o…
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
Ganesh Jawahar, Haichuan Yang, Yunyang Xiong +10
Weight-sharing supernets are crucial for performance estimation in cutting-edge neural architecture search (NAS) frameworks. Despite their ability to generate diverse subnetworks w…
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation
Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu +6
Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing works in MoE mostly consider a homogeneous design wher…
Automatic Detection of Entity-Manipulated Text using Factual Knowledge
Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan
In this work, we focus on the problem of distinguishing a human written news article from a news article that is created by manipulating entities in a human written news article (e…
Contrastive Learning of Sociopragmatic Meaning in Social Media
Chiyu Zhang, Muhammad Abdul-Mageed, Ganesh Jawahar
Recent progress in representation and contrastive learning in NLP has not widely considered the class of \textit{sociopragmatic meaning} (i.e., meaning in interaction within differ…
Automatic Detection of Machine Generated Text: A Critical Survey
Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan
Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically gene…