104 citations · 189 across the 14 of their papers we have counts for
14 papers
Steering LLMs for Culturally Localized Generation
Simran Khanuja, Hongbin Liu, Shujian Zhang +4
LLMs are deployed globally, yet produce responses biased towards cultures with abundant training data. Existing cultural localization approaches such as prompting or post-training…
Fantastic Reasoning Behaviors and Where to Find Them: Unsupervised Discovery of the Reasoning Process
Zhenyu Zhang, Shujian Zhang, John Lambert +6
Despite the growing reasoning capabilities of recent large language models (LLMs), their internal mechanisms during the reasoning process remain underexplored. Prior approaches oft…
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Lillian Zhou, Yuxin Ding, Mingqing Chen +5
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech. As language evolves and new terms come into use, these models can become ou…
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning
Sandy Ritchie, You-Chi Cheng, Mingqing Chen +4
Almost none of the 2,000+ languages spoken in Africa have widely available automatic speech recognition systems, and the required data is also only available for a few languages. W…
UserLibri: A Dataset for ASR Personalization Using Only Text
Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani +6
Personalization of speech models on mobile devices (on-device personalization) is an active area of research, but more often than not, mobile devices have more text-only data than…
Online Model Compression for Federated Learning with Large Models
Tien-Ju Yang, Yonghui Xiao, Giovanni Motta +3
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Onli…