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README.md

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+# Dataset Card for "propqa-tp"
+
+### Dataset Summary
+
+The PopQA-TP (PopQA Templated Paraphrases) is a dataset derived from PopQA (https://huggingface.co/datasets/akariasai/PopQA), created for the paper "Predicting Question-Answering Performance of Large Language Models
+through Semantic Consistency".  PopQA-TP takes each question in PopQA and paraphrases it using each of several manually-created templates specific to each question category.  The paper investigates the connection between
+the semantic consistency of generated answers to each question's paraphrases and the accuracy (correctness) of the generated answer to the original question, evaluated by string match to one of the ground
+truth answers.  
+
+### Data Instances
+
+#### popqa-tp
+
+- **Size of downloaded dataset file:** 29 MB
+
+### Data Fields
+#### popqa-tp
+- `paraphrase` (string): paraphrased of question from PopQ.
+- `prop` (string): relationship type category of question.
+- `template_id` (integer): integer ID of the paraphrase template used.  Value of 0 indicates it is the original question form from PopQA.
+- `possible_answers' (list of strings): a list of the gold answers.
+- `id' (integer): original ID of question from PopQA
+
+### Citation Information
+```
+@inproceedings{rabinovich2023predicting,
+  title={Predicting Question-Answering Performance of Large Language Models
+through Semantic Consistency},
+  author={Ella Rabinovich, Samuel Ackerman, Orna Raz, Eitan Farchi, Ateret Anaby-Tavor},
+  booktitle = "Proceedings of the 3rd Version of the Generation, Evaluation & Metrics (GEM) Workshop of The 2023 Conference on Empirical Methods in Natural Language Processing",
+  publisher = "Association for Computational Linguistics",
+  year={2023},}
+}
+```