163 citations · 240 across the 24 of their papers we have counts for
7 papers · 1 filter
A Length-Extrapolatable Transformer
Yutao Sun, Li Dong, Barun Patra +6
Position modeling plays a critical role in Transformers. In this paper, we focus on length extrapolation, i.e., training on short texts while evaluating longer sequences. We define…
TorchScale: Transformers at Scale
Shuming Ma, Hongyu Wang, Shaohan Huang +8
Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with be…
Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning
Barun Patra, Saksham Singhal, Shaohan Huang +5
In this paper, we elaborate upon recipes for building multilingual representation models that are not only competitive with existing state-of-the-art models but are also more param…
Foundation Transformers
Hongyu Wang, Shuming Ma, Shaohan Huang +12
A big convergence of model architectures across language, vision, speech, and multimodal is emerging. However, under the same name "Transformers", the above areas use different imp…
Language Model Decoding as Likelihood-Utility Alignment
Martin Josifoski, Maxime Peyrard, Frano Rajic +8
A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re…
On Efficiently Acquiring Annotations for Multilingual Models
Joel Ruben Antony Moniz, Barun Patra, Matthew R. Gormley
When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among…
On the Representation Collapse of Sparse Mixture of Experts
Zewen Chi, Li Dong, Shaohan Huang +9
Sparse mixture of experts provides larger model capacity while requiring a constant computational overhead. It employs the routing mechanism to distribute input tokens to the best-…