activity
20182022
most citedContinued Pretraining for Better Zero- and Few-Shot Promptability

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CL20221 cited

Continued Pretraining for Better Zero- and Few-Shot Promptability

Zhaofeng Wu, Robert L. Logan, Pete Walsh +4

Recently introduced language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters. Neverthele…

cs.CL2021

Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

Jesse Dodge, Maarten Sap, Ana Marasović +5

Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora availabl…

cs.CL2020

A Simple Yet Strong Pipeline for HotpotQA

Dirk Groeneveld, Tushar Khot, Mausam +1

State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named enti…

cs.CL2019

From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project

Peter Clark, Oren Etzioni, Daniel Khashabi +11

AI has achieved remarkable mastery over games such as Chess, Go, and Poker, and even Jeopardy, but the rich variety of standardized exams has remained a landmark challenge. Even in…

cs.CL2018

Construction of the Literature Graph in Semantic Scholar

Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula +20

We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting…