376 citations · 612 across the 4 of their papers we have counts for
5 papers
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru +15
Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few…
Large scale distributed neural network training through online distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos +3
Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased test-time cost (for ensembles) a…
Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra, George Tucker, Jan Chorowski +2
We systematically explore regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been show…
Batch Normalized Recurrent Neural Networks
César Laurent, Gabriel Pereyra, Philémon Brakel +2
Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies. However, they are computationally expensive to tra…