You're currently viewing an old version of this dataset. To see the current version, click here.

Learning to summarize with human feedback

The paper presents a study on the impact of synthetic data on large language models (LLMs) and proposes a method to steer LLMs towards desirable non-differentiable attributes.

Data and Resources

This dataset has no data

Cite this as

Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei (2024). Dataset: Learning to summarize with human feedback. https://doi.org/10.57702/bakxgny5

Private DOI This DOI is not yet resolvable.
It is available for use in manuscripts, and will be published when the Dataset is made public.

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2407.01490
Citation
  • https://doi.org/10.48550/arXiv.2312.09244
Author Nisan Stiennon
More Authors
Long Ouyang
Jeffrey Wu
Daniel Ziegler
Ryan Lowe
Chelsea Voss
Alec Radford
Dario Amodei