
### Acknowledgements
The PUZZLEQA is scraped from [NPR Sunday Puzzle Official Website](https://www.npr.org/series/4473090/sunday-puzzle) and [NPR Puzzle Synopsis](https://groups.google.com/g/nprpuzzle), 
made by a group of fans by running a mailing list that distributed questions and answers for each week’s puzzle.
The authors of the dataset cleaned the data and made some multiple choice based on the question and answers.

### Creation
The Multiple Choice Dataset is generated from PUZZLEQA dataset using the following algorithm. 
1. Read the fr_big_exp.tsv.tsv file
2. Group rule-question-answer triples in a given Sunday together (so the rules of each question will be the same) 
3. For each question, randomly select three other answers from answers on the same Sunday. Shuffle 3 selected answers with the correct answer for the given question to obtain 4 choices for this question. \\
4. identify the correct answer for the given question as the "gold" answer.

Recent.tsv is the dataset based on the NPR PUZZLE in 2023.

# Citation
@inproceedings{zhao2023solving,
      title={Solving and Generating NPR Sunday Puzzles with Large Language Models}, 
      author={Jingmiao Zhao and Carolyn Jane Anderson},
      year={2023},
      eprint={2306.12255},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
language:en license:apache-2.0 arxiv:2306.12255 region:us
{"language": ["en"], "license": "apache-2.0"}
TAGS
#language-English #license-apache-2.0 #arxiv-2306.12255 #region-us 


### Acknowledgements
The PUZZLEQA is scraped from NPR Sunday Puzzle Official Website and NPR Puzzle Synopsis, 
made by a group of fans by running a mailing list that distributed questions and answers for each week’s puzzle.
The authors of the dataset cleaned the data and made some multiple choice based on the question and answers.

### Creation
The Multiple Choice Dataset is generated from PUZZLEQA dataset using the following algorithm. 
1. Read the fr_big_exp.URL file
2. Group rule-question-answer triples in a given Sunday together (so the rules of each question will be the same) 
3. For each question, randomly select three other answers from answers on the same Sunday. Shuffle 3 selected answers with the correct answer for the given question to obtain 4 choices for this question. \\
4. identify the correct answer for the given question as the "gold" answer.

URL is the dataset based on the NPR PUZZLE in 2023.

@inproceedings{zhao2023solving,
      title={Solving and Generating NPR Sunday Puzzles with Large Language Models}, 
      author={Jingmiao Zhao and Carolyn Jane Anderson},
      year={2023},
      eprint={2306.12255},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
### Acknowledgements
The PUZZLEQA is scraped from NPR Sunday Puzzle Official Website and NPR Puzzle Synopsis, 
made by a group of fans by running a mailing list that distributed questions and answers for each week’s puzzle.
The authors of the dataset cleaned the data and made some multiple choice based on the question and answers. ### Creation
The Multiple Choice Dataset is generated from PUZZLEQA dataset using the following algorithm. 
1. Read the fr_big_exp.URL file
2. Group rule-question-answer triples in a given Sunday together (so the rules of each question will be the same) 
3. For each question, randomly select three other answers from answers on the same Sunday. Shuffle 3 selected answers with the correct answer for the given question to obtain 4 choices for this question. \\
4. identify the correct answer for the given question as the "gold" answer.

URL is the dataset based on the NPR PUZZLE in 2023.

@inproceedings{zhao2023solving,
      title={Solving and Generating NPR Sunday Puzzles with Large Language Models}, 
      author={Jingmiao Zhao and Carolyn Jane Anderson},
      year={2023},
      eprint={2306.12255},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
TAGS
#language-English #license-apache-2.0 #arxiv-2306.12255 #region-us 
 ### Acknowledgements
The PUZZLEQA is scraped from NPR Sunday Puzzle Official Website and NPR Puzzle Synopsis, 
made by a group of fans by running a mailing list that distributed questions and answers for each week’s puzzle.
The authors of the dataset cleaned the data and made some multiple choice based on the question and answers. ### Creation
The Multiple Choice Dataset is generated from PUZZLEQA dataset using the following algorithm. 
1. Read the fr_big_exp.URL file
2. Group rule-question-answer triples in a given Sunday together (so the rules of each question will be the same) 
3. For each question, randomly select three other answers from answers on the same Sunday. Shuffle 3 selected answers with the correct answer for the given question to obtain 4 choices for this question. \\
4. identify the correct answer for the given question as the "gold" answer.

URL is the dataset based on the NPR PUZZLE in 2023.

@inproceedings{zhao2023solving,
      title={Solving and Generating NPR Sunday Puzzles with Large Language Models}, 
      author={Jingmiao Zhao and Carolyn Jane Anderson},
      year={2023},
      eprint={2306.12255},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
passage: TAGS
#language-English #license-apache-2.0 #arxiv-2306.12255 #region-us 
### Acknowledgements
The PUZZLEQA is scraped from NPR Sunday Puzzle Official Website and NPR Puzzle Synopsis, 
made by a group of fans by running a mailing list that distributed questions and answers for each week’s puzzle.
The authors of the dataset cleaned the data and made some multiple choice based on the question and answers.### Creation
The Multiple Choice Dataset is generated from PUZZLEQA dataset using the following algorithm. 
1. Read the fr_big_exp.URL file
2. Group rule-question-answer triples in a given Sunday together (so the rules of each question will be the same) 
3. For each question, randomly select three other answers from answers on the same Sunday. Shuffle 3 selected answers with the correct answer for the given question to obtain 4 choices for this question. \\
4. identify the correct answer for the given question as the "gold" answer.

URL is the dataset based on the NPR PUZZLE in 2023.

@inproceedings{zhao2023solving,
      title={Solving and Generating NPR Sunday Puzzles with Large Language Models}, 
      author={Jingmiao Zhao and Carolyn Jane Anderson},
      year={2023},
      eprint={2306.12255},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}