3 citations · 5 across the 6 of their papers we have counts for
6 papers
Multi-Satellite Beam Hopping and Power Allocation Using Deep Reinforcement Learning
Xia Xie, Kexin Fan, Wenfeng Deng +2
In non-geostationary orbit (NGSO) satellite communication systems, effectively utilizing beam hopping (BH) technology is crucial for addressing uneven traffic demands. However, opt…
Sequential Editing for Lifelong Training of Speech Recognition Models
Devang Kulshreshtha, Saket Dingliwal, Brady Houston +2
Automatic Speech Recognition (ASR) traditionally assumes known domains, but adding data from a new domain raises concerns about computational inefficiencies linked to retraining mo…
DEM: Distribution Edited Model for Training with Mixed Data Distributions
Dhananjay Ram, Aditya Rawal, Momchil Hardalov +2
Training with mixed data distributions is a common and important part of creating multi-task and instruction-following models. The diversity of the data distributions and cost of j…
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
Hossein Aboutalebi, Hwanjun Song, Yusheng Xie +7
Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs. Previous…
Eliciting Better Multilingual Structured Reasoning from LLMs through Code
Bryan Li, Tamer Alkhouli, Daniele Bonadiman +2
The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we…
DeAL: Decoding-time Alignment for Large Language Models
James Y. Huang, Sailik Sengupta, Daniele Bonadiman +6
Large Language Models (LLMs) are nowadays expected to generate content aligned with human preferences. Current work focuses on alignment at model training time, through techniques…