Explaining Patterns in Data with Language Models via Interpretable Autoprompting

By Chandan Singh, John X. Morris, Jyoti Aneja, Alexander M. Rush, Jianfeng Gao. 2022

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Large Language Models, Prompting & Reasoning

How researchers see this paper

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

In Artificial Intelligence

1. David Vilar
2. Colin Cherry
3. George F. Foster
4. Viresh Ratnakar
5. Markus Freitag
6. Jiaming Luo
7. Felix Stahlberg
8. Orhan Firat

In Large Language Models

1. Markus Freitag

Citations in the corpus

• Prompting PaLM for Translation: Assessing Strategies and Performance (2022, methodology): “Hard prompts have also been used to analyze model capabilities (Garg et al., 2022; Li et al., 2022a), the role of data (Singh et al., 2022), and the nature of prompting itself (Min et al., 2022; Wei et al., 2022).”