Papers by Timo Schrader
QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios (2024.emnlp-main)
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| Challenge: | Existing probabilistic reasoning datasets require the model to only rank textual alternatives or use limited set of templates. |
| Approach: | They propose a question-answering dataset that uses probabilistic rules to express degrees of certainty. |
| Outcome: | The proposed model outperforms existing models on all reasoning types . it is available on Github and is expected to be used in clinical documentation . |