Talk to Transformer

By Mark Amerika. 2021

5 citations recorded by the source corpus.

Subfields: Cultural Studies, Sociology and Political Science, Visual Arts and Performing Arts, Large Language Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cultural Studies

1. Amanda Askell
2. Ariel Herbert-Voss
3. Irene Solaiman
4. Noah A. Smith
5. Yejin Choi
6. Samuel Gehman
7. Jack Clark
8. Jasmine Wang

In Sociology and Political Science

1. Maarten Sap
2. Yejin Choi
3. Noah A. Smith
4. Suchin Gururangan
5. Amanda Askell
6. Ariel Herbert-Voss
7. Irene Solaiman
8. Samuel Gehman

In Visual Arts and Performing Arts

1. Amanda Askell
2. Ariel Herbert-Voss
3. Irene Solaiman
4. Noah A. Smith
5. Yejin Choi
6. Samuel Gehman
7. Jack Clark
8. Jasmine Wang

In Large Language Models

1. Yejin Choi
2. Noah A. Smith
3. Maarten Sap
4. Suchin Gururangan
5. Luke S. Zettlemoyer
6. Jeff Wu
7. Alec Radford
8. Samuel Gehman

Citations in the corpus

• Release Strategies and the Social Impacts of Language Models (2019, methodology): “Steadily improved tools for interacting with language models, such as the Talk to Transformer [34] and Write with Transformer [17] interfaces, will broaden the number of actors who can use language models in a range of different ways.”
• RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models (2020, methodology): “To systematically evaluate and compare the generations from language models, we create REALTOXICITYPROMPTS as a testbed for toxicity in conditional language generation that mirrors real world applications (e.g., autocomplete systems; Chen et al., 2019; King, 2019).”