1.5k citations · 1.7k across the 4 of their papers we have counts for
9 papers · 1 filter
Text and Code Embeddings by Contrastive Pre-Training
Arvind Neelakantan, Tao Xu, Raul Puri +22
Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use c…
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…
BioMegatron: Larger Biomedical Domain Language Model
Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina +4
There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than t…
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models
Peng Xu, Mostofa Patwary, Mohammad Shoeybi +4
Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel fr…
Large Scale Multi-Actor Generative Dialog Modeling
Alex Boyd, Raul Puri, Mohammad Shoeybi +2
Non-goal oriented dialog agents (i.e. chatbots) aim to produce varying and engaging conversations with a user; however, they typically exhibit either inconsistent personality acros…
Training Question Answering Models From Synthetic Data
Raul Puri, Ryan Spring, Mostofa Patwary +2
Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a consider…