299 citations · 334 across the 6 of their papers we have counts for
13 papers
Evaluating Parameter Efficient Learning for Generation
Peng Xu, Mostofa Patwary, Shrimai Prabhumoye +6
Parameter efficient learning methods (PERMs) have recently gained significant attention as they provide an efficient way for pre-trained language models (PLMs) to adapt to a downst…
Multi-Stage Prompting for Knowledgeable Dialogue Generation
Zihan Liu, Mostofa Patwary, Ryan Prenger +4
Existing knowledge-grounded dialogue systems typically use finetuned versions of a pretrained language model (LM) and large-scale knowledge bases. These models typically fail to ge…
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
Shaden Smith, Mostofa Patwary, Brandon Norick +17
Pretrained general-purpose language models can achieve state-of-the-art accuracies in various natural language processing domains by adapting to downstream tasks via zero-shot, few…
Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
Deepak Narayanan, Mohammad Shoeybi, Jared Casper +9
Large language models have led to state-of-the-art accuracies across a range of tasks. However, training these models efficiently is challenging for two reasons: a) GPU memory capa…
End-to-End Training of Neural Retrievers for Open-Domain Question Answering
Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi +4
Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsu…
Local Knowledge Powered Conversational Agents
Sashank Santhanam, Wei Ping, Raul Puri +3
State-of-the-art conversational agents have advanced significantly in conjunction with the use of large transformer-based language models. However, even with these advancements, co…