COCO-LM: Correcting and Contrasting Text Sequences for Language Model
Pretraining

COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

16 February 2021

Paul N. Bennett

Xia Song

Papers citing "COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining"

8 / 8 papers shown

Title
Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning Yuhan Liu Lin Ning Neo Wu Karan Singhal Philip Mansfield D. Berlowitz Sushant Prakash Bradley Green SSL 36 31 0 02 May 2025
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 228 1,593 0 31 Dec 2020
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding Yanru Qu Dinghan Shen Yelong Shen Sandra Sajeev Jiawei Han Weizhu Chen 110 58 0 16 Oct 2020
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks Nandan Thakur Nils Reimers Johannes Daxenberger Iryna Gurevych 175 190 0 16 Oct 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 215 2,998 0 23 Jan 2020
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference Timo Schick Hinrich Schütze 237 1,375 0 21 Jan 2020
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 237 1,412 0 17 Sep 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 283 6,003 0 20 Apr 2018