38 citations · 60 across the 10 of their papers we have counts for
5 papers · 1 filter
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald +13
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, ar…
Federated Learning with Noisy User Feedback
Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin +5
Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. This has led to growing concerns over user priv…
Training Mixed-Domain Translation Models via Federated Learning
Peyman Passban, Tanya Roosta, Rahul Gupta +2
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning…
Differentially Private Decoding in Large Language Models
Jimit Majmudar, Christophe Dupuy, Charith Peris +3
Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-traine…
Canary Extraction in Natural Language Understanding Models
Rahil Parikh, Christophe Dupuy, Rahul Gupta
Natural Language Understanding (NLU) models can be trained on sensitive information such as phone numbers, zip-codes etc. Recent literature has focused on Model Inversion Attacks (…