| # Dataset Card for PopQA | |
| ## Dataset Summary | |
| PopQA is a large-scale open-domain question answering (QA) dataset, consisting of 14k entity-centric QA pairs. Each question is created by converting a knowledge tuple retrieved from Wikidata using a template. Each question come with the original `subject_entitiey`, `object_entity`and `relationship_type` annotation, as well as Wikipedia monthly page views. | |
| ## Languages | |
| The dataset contains samples in English only. | |
| ## Dataset Structure | |
| ### Data Instances | |
| - Size of downloaded dataset file: 5.2 MB | |
| ## Data Fields | |
| - `id`: question id | |
| - `subj`: subject entity name | |
| - `prop`: relationship type | |
| - `obj`: object entity name | |
| - `subj_id`: Wikidata ID of the subject entity | |
| - `prop_id`: Wikidata relationship type ID | |
| - `obj_id`: Wikidata ID of the object entity | |
| - `s_aliases`: aliases of the subject entity | |
| - `o_aliases`: aliases of the object entity | |
| - `s_uri`: Wikidata URI of the subject entity | |
| - `o_uri`: Wikidata URI of the object entity | |
| - `s_wiki_title`: Wikipedia page title of the subject entity | |
| - `o_wiki_title`: Wikipedia page title of the object entity | |
| - `s_pop`: Wikipedia monthly pageview of the subject entity | |
| - `o_pop`: Wikipedia monthly pageview of the object entity | |
| - `question`: PopQA question | |
| - `possible_answers`: a list of the gold answers. | |
| ## Citation Information | |
| ``` | |
| @article{ mallen2023llm_memorization , | |
| title={When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories }, | |
| author={ Mallen, Alex and Asai,Akari and Zhong, Victor and Das, Rajarshi and Hajishirzi, Hannaneh and Khashabi, Daniel}, | |
| journal={ arXiv preprint }, | |
| year={ 2022 } | |
| } | |
| ``` | |