607 citations · 1.2k across the 31 of their papers we have counts for
6 papers · 1 filter
OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal +16
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computatio…
Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders
Xiang Kong, Adithya Renduchintala, James Cross +3
Recent work in multilingual translation advances translation quality surpassing bilingual baselines using deep transformer models with increased capacity. However, the extra latenc…
Lifting the Curse of Multilinguality by Pre-training Modular Transformers
Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin +4
Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this i…
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Badr AlKhamissi, Faisal Ladhak, Srini Iyer +7
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next. It…
Unified Speech-Text Pre-training for Speech Translation and Recognition
Yun Tang, Hongyu Gong, Ning Dong +8
We describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method incorporates four sel…
Efficient Language Modeling with Sparse all-MLP
Ping Yu, Mikel Artetxe, Myle Ott +4
All-MLP architectures have attracted increasing interest as an alternative to attention-based models. In NLP, recent work like gMLP shows that all-MLPs can match Transformers in la…