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

Detecting Hallucinated Content in Conditional Neural Sequence Generation

Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input.

Data and Resources

This dataset has no data

Cite this as

Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Paco Guzman, Luke Zettlemoyer (2024). Dataset: Detecting Hallucinated Content in Conditional Neural Sequence Generation. https://doi.org/10.57702/9ymvoxih

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.2011.02593
Author	Chunting Zhou
More Authors	Graham Neubig Jiatao Gu Mona Diab Paco Guzman Luke Zettlemoyer
Homepage	https://github.com/violet-zct/fairseq-detect-hallucination