QA Datasets

QA datasets are collections of questions and their corresponding answers used to train and evaluate question-answering (QA) models, primarily focusing on improving the accuracy and robustness of these models across diverse domains and languages. Current research emphasizes creating datasets that address specific challenges like temporal ambiguity, handling multi-modal information (text, images, etc.), and evaluating model faithfulness and abstention behavior. These datasets, coupled with techniques like retrieval-augmented generation (RAG) and fine-tuning with methods such as LoRA, are crucial for advancing QA capabilities and enabling applications in healthcare, education, and scientific knowledge discovery.