1.2k citations · 1.4k across the 8 of their papers we have counts for
14 papers
Transcending Scaling Laws with 0.1% Extra Compute
Yi Tay, Jason Wei, Hyung Won Chung +13
Scaling language models improves performance but comes with significant computational costs. This paper proposes UL2R, a method that substantially improves existing language models…
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Mirac Suzgun, Nathan Scales, Nathanael Schärli +8
BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. Language models have alre…
Language Models are Multilingual Chain-of-Thought Reasoners
Freda Shi, Mirac Suzgun, Markus Freitag +9
We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250…
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…
What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow +5
Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly traine…
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…