Laurie/faithfulness-qa-dataset

Faithfulness-QA: A Counterfactual Entity Substitution Dataset for Training Context-Faithful RAG Models Overview Faithfulness-QA is a large-scale dataset of 99,094 question-answer pairs designed to train and evaluate the faithfulness of Retrieval-Augmented Generation (RAG) models to retrieved context. The core idea is counterfactual entity substitution: for each QA sample, we replace the answer-bearing entity in the context with a type-consistent alternative… See the full description on the dataset page: https://huggingface.co/datasets/Laurie/faithfulness-qa-dataset.