datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hallucination-detection
Dataset Summary
Hallucination Detection dataset is a specialized dataset designed to evaluate language models' tendency to hallucinate (generate factually incorrect or unsupported information) in the Earth Observation (EO) domain. Unlike typical QA datasets that focus on correctness, this dataset contains deliberately hallucinated answers with detailed annotations marking which portions of the text are hallucinated.
This dataset was introduced as part of the paper EVE: A… See the full description on the dataset page: https://huggingface.co/datasets/eve-esa/hallucination-detection.med-hallucination-detection
Medical hallucination detection
A dataset for training a small model to detect hallucinations in medical answers
and explain why, by checking each answer against the context it should be
grounded in. Each row is a (question, answer, context) triple with a row_type:
not_hallucinated -- the answer is grounded in its context.
hallucinated -- the answer is not (sourced separately; see below).
The not_hallucinated split (this build)
Derived from MedQuAD, a collection… See the full description on the dataset page: https://huggingface.co/datasets/Certops/med-hallucination-detection.med-hallucination-detection-unfiltered
Medical hallucination detection (unfiltered)
A dataset for training a small model to detect hallucinations in medical answers
and explain why, by checking each answer against the context it should be
grounded in. Each row is a (question, answer, context) triple labelled row_type.
This is the unfiltered union of two sources: 7,464 grounded positives and
10,000 planted-hallucination negatives. It is the raw pool before sampling and
judging -- the downstream step samples from here… See the full description on the dataset page: https://huggingface.co/datasets/Certops/med-hallucination-detection-unfiltered.llm-hallucination-detection