2.1k citations · 5.7k across the 22 of their papers we have counts for
7 papers · 1 filter
PaLM-E: An Embodied Multimodal Language Model
Danny Driess, Fei Xia, Mehdi S. M. Sajjadi +19
Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding.…
Efficiently Scaling Transformer Inference
Reiner Pope, Sholto Douglas, Aakanksha Chowdhery +7
We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long seque…
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre +32
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…
Understanding HTML with Large Language Models
Izzeddin Gur, Ofir Nachum, Yingjie Miao +6
Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. Yet, their capabilities for HTML understanding -- i.e., parsing the raw HTML…
Scaling Up Models and Data with and
Adam Roberts, Hyung Won Chung, Anselm Levskaya +40
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can…
Sparse is Enough in Scaling Transformers
Sebastian Jaszczur, Aakanksha Chowdhery, Afroz Mohiuddin +4
Large Transformer models yield impressive results on many tasks, but are expensive to train, or even fine-tune, and so slow at decoding that their use and study becomes out of reac…