157 citations · 417 across the 10 of their papers we have counts for
17 papers
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…
Relational Memory Augmented Language Models
Qi Liu, Dani Yogatama, Phil Blunsom
We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve r…
Balancing Average and Worst-case Accuracy in Multitask Learning
Paul Michel, Sebastian Ruder, Dani Yogatama
When training and evaluating machine learning models on a large number of tasks, it is important to not only look at average task accuracy -- which may be biased by easy or redunda…
Random Feature Attention
Hao Peng, Nikolaos Pappas, Dani Yogatama +3
Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their core is an attention function which models pairwise interactions between the inputs at e…
Finetuning Pretrained Transformers into RNNs
Jungo Kasai, Hao Peng, Yizhe Zhang +6
Transformers have outperformed recurrent neural networks (RNNs) in natural language generation. But this comes with a significant computational cost, as the attention mechanism's c…
Adaptive Semiparametric Language Models
Dani Yogatama, Cyprien de Masson d'Autume, Lingpeng Kong
We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our…