Learning from Natural Language Feedback

By J'er'emy Scheurer, Jon Ander Campos, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, Ethan Perez. 2024

29 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Alignment & RLHF

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. Ruiqi Zhong
2. Charles Burton Snell
3. Daniel Klein
4. Luke S. Zettlemoyer
5. Christopher D. Manning
6. Percy Liang
7. Hannaneh Hajishirzi
8. Mohit Bansal

In Natural Language Processing

1. Ruiqi Zhong
2. Charles Burton Snell
3. Daniel Klein
4. Christopher D. Manning
5. Luke S. Zettlemoyer
6. Percy Liang
7. Mirella Lapata
8. Graham Neubig

In Alignment & RLHF

1. Daniel Klein

Citations in the corpus

• Learning by Distilling Context (2022, background): “However, we anticipate that future language models be more responsive to natural language explanations (Kaplan et al., 2020; Scheurer et al., 2022), hence improving the performance of context distillation.”