1k citations · 1.6k across the 19 of their papers we have counts for
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Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?
Yi Tay, Mostafa Dehghani, Samira Abnar +7
There have been a lot of interest in the scaling properties of Transformer models. However, not much has been done on the front of investigating the effect of scaling properties of…
Questions Are All You Need to Train a Dense Passage Retriever
Devendra Singh Sachan, Mike Lewis, Dani Yogatama +3
We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data. Dense retrieval is a central challen…
Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani +13
Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictabl…
Language Models Can See: Plugging Visual Controls in Text Generation
Yixuan Su, Tian Lan, Yahui Liu +5
Generative language models (LMs) such as GPT-2/3 can be prompted to generate text with remarkable quality. While they are designed for text-prompted generation, it remains an open…
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning
Paul Pu Liang, Yiwei Lyu, Xiang Fan +6
Many real-world problems are inherently multimodal, from spoken language, gestures, and paralinguistics humans use to communicate, to force, proprioception, and visual sensors on r…
A Contrastive Framework for Neural Text Generation
Yixuan Su, Tian Lan, Yan Wang +3
Text generation is of great importance to many natural language processing applications. However, maximization-based decoding methods (e.g. beam search) of neural language models o…