| license: mit | |
| task_categories: | |
| - question-answering | |
| - text-classification | |
| - text-retrieval | |
| language: | |
| - en | |
| tags: | |
| - academic | |
| - questions | |
| - evidence | |
| - papers | |
| size_categories: | |
| - 1K<n<10K | |
| # ELAIPBench Dataset | |
| ## Description | |
| This dataset contains academic questions with evidence passages extracted from research papers. Each question is paired with a relevant passage from the source paper that provides evidence for answering the question.It was officially adopted as the dataset for the [CCKS 2025 Academic Paper Question Answering Challenge](https://tianchi.aliyun.com/competition/entrance/532359). | |
| ## Dataset Structure | |
| The dataset contains 403 questions with the following fields: | |
| - `paper_id`: ID of the source paper (corresponds to PDF filename in papers.zip) | |
| - `question_type`: Type of question (SA-MCQ, MA-MCQ, etc.) | |
| - `question`: The question text | |
| - `answer`: The correct answer | |
| - `relevant_passage`: Evidence passage extracted from the paper | |
| - `paper_content`: Full text content of the source paper | |
| ## Usage | |
| ```python | |
| from datasets import load_dataset | |
| # Load the dataset | |
| dataset = load_dataset("KangKang625/ELAIPBench") | |
| # Access the data | |
| data = dataset['test'] | |
| print(f"Number of questions: {len(data)}") | |
| print(f"First question: {data[0]['question']}") | |
| print(f"Paper content length: {len(data[0]['paper_content'])}") | |
| ``` | |
| ## Citation | |
| If you use this dataset, please cite the original ELAIPBench paper. | |
| ## License | |
| MIT License | |