Duplicate from akariasai/PopQA

Co-authored-by: Akari Asai <akariasai@users.noreply.huggingface.co>

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README.md

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+# 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 }
+}
+```