datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
movie_rationalesThe movie rationale dataset contains human annotated rationales for movie
reviews.LitBench-RationalesIf you are the author of any comment in this dataset and would like it removed, please contact us and we will comply promptly.
fair-rationalesExplainability methods are used to benchmark
the extent to which model predictions align
with human rationales i.e., are 'right for the
right reasons'. Previous work has failed to acknowledge, however,
that what counts as a rationale is sometimes subjective. This paper
presents what we think is a first of its kind, a
collection of human rationale annotations augmented with the annotators demographic information.SFT_PN_Rationales
SFT Dataset
generated from Qwen/Qwen3-VL-32B-Instruct
verified from OpenGVLab/InternVL3-78B
Domain Distribution of Positive/Negative Rationales
Per-Dataset Positive/Negative Rationale Counts by Domain
litbench-rationales-gpt4
LitBench Rationales - GPT-4 Rubric Evaluations
This dataset contains new rationales for story pair evaluations from the LitBench dataset, generated using GPT-4 with a structured rubric-based evaluation approach.
Evaluation Rubric
The rationales were generated using a 5-criterion rubric:
Creativity & Originality (25 points): Uniqueness of concept, innovative elements, fresh perspective
Writing Quality & Style (25 points): Prose quality, voice consistency, grammar and… See the full description on the dataset page: https://huggingface.co/datasets/SAA-Lab/litbench-rationales-gpt4.LitBench-new-rationales
Dataset Card for "LitBench-Rationales-GPT4-Complete"
More Information needed
climate_fever_rationalesThe Climate-Fever dataset was first collected and published by Diggelmann et al, 2020.For our study, we are interested in token-level rationales which are not available from the initial publication of Climate-Fever. Therefore, we manually selected a subset of 102 claims (510 claim-evidence pairs) based on clarity of the claim formulation and balanced claim labels. Each sample was annotated on token-level by 3 annotators as either supporting the claim (label=1), contradicting the claim… See the full description on the dataset page: https://huggingface.co/datasets/stephaniebrandl/climate_fever_rationales.movie_rationales_truncatedsciq_aquarat_generated_rationalessocio-moral-image-rationales
Socio-Moral Image Rationales
This is a collection of machine-generated and human-labeled explanations for immorality in images.
The images are source from the Socio-Moral Image Database (SMID) and limited to the ones displaying immoral content (SMID moral mean <= 2.0).
Sampled explanations were generated by vision-language model using the ILLUME paradigm presented in ILLUME: Rationalizing Vision-Language Models through Human Interactions.
Explanations are rated by human annotators… See the full description on the dataset page: https://huggingface.co/datasets/AIML-TUDA/socio-moral-image-rationales.CoT-Collection-RationalesSuperBEIR-categories-with-rationales-gflRationales-Alpaca-with-Choicesgpt_rationales_for_mongolian_newsalpaca-rationalescode-rationalesCoT-Collection-Rationales-newexplainable_ai_rationales.jsonltrivia_qa_with_rationaleswp_train_with_rationales_0425forward-rationales-0429wp_paired_with_4.1_rationales_0501wp_train_rationales_0504