timo-pierre-schrader/QUITE
Dataset Card for QUITE Dataset Description 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. For example, take the following statement from QUITE: If Plcg is in a high state, PIP3 appears in a low state in 42% of all… See the full description on the dataset page: https://huggingface.co/datasets/timo-pierre-schrader/QUITE.
| Author / organization | timo-pierre-schrader |
|---|---|
| License (as declared) | cc-by-4.0 |
| Downloads | 148 (as reported by Hugging Face) |
| Likes | 3 |
| Created | 10 November 2024 |
| Last updated at source | 8 November 2024 |
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- 17 Oct 2026 Description: Dataset Card for QUITE Dataset Description 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 → Dataset Card for QUITE Dataset Description 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
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