715 citations · 1.2k across the 20 of their papers we have counts for
20 papers
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models
David Wingate, Mohammad Shoeybi, Taylor Sorensen
We explore the idea of compressing the prompts used to condition language models, and show that compressed prompts can retain a substantive amount of information about the original…
Leveraging Large Language Models for Multiple Choice Question Answering
Joshua Robinson, Christopher Michael Rytting, David Wingate
While large language models (LLMs) like GPT-3 have achieved impressive results on multiple choice question answering (MCQA) tasks in the zero, one, and few-shot settings, they gene…
Out of One, Many: Using Language Models to Simulate Human Samples
Lisa P. Argyle, Ethan C. Busby, Nancy Fulda +3
We propose and explore the possibility that language models can be studied as effective proxies for specific human sub-populations in social science research. Practical and researc…
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning
Christopher Michael Rytting, David Wingate
Large natural language models (such as GPT-3 or T5) demonstrate impressive abilities across a range of general NLP tasks. Here, we show that the knowledge embedded in such models p…
Towards Neural Programming Interfaces
Zachary C. Brown, Nathaniel Robinson, David Wingate +1
It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language g…
Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads
Erich Mielke, Eric Townsend, David Wingate +1
Human teams are able to easily perform collaborative manipulation tasks. However, for a robot and human to simultaneously manipulate an extended object is a difficult task using ex…