12 citations · 15 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
Knowledge Enhanced Contextual Word Representations
Matthew E. Peters, Mark Neumann, Robert L. Logan +4
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember fa…
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling
Robert L. Logan, Nelson F. Liu, Matthew E. Peters +2
Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of rememberin…
PoMo: Generating Entity-Specific Post-Modifiers in Context
Jun Seok Kang, Robert L. Logan, Zewei Chu +5
We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-mo…