Datasets › QUITE
QUITE (Quantifying Uncertainty in natural language Text)
QUITE (Quantifying Uncertainty in natural language Text) is an entirely new benchmark that allows for assessing the capabilities of neural language model-based systems w.r.t. to Bayesian reasoning on a large set of input text that describes probabilistic relationships in natural language text.
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 2 papers for it but never published that list.
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- QUITE
1 variant name, as the archive lists them.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections