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The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions.
Language models are few-shot learners
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Semantic similarity of distractors in multiple-choice tests: Extrinsic evaluation
Ruslan Mitkov, Le An Ha, Andrea Varga, and Luz Rello. 2009 · 2009
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Semi-automatic generation of cloze question distractors effect of students’ l1
Juan Pino and Maxine Eskenazi. 2009 · 2009
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Better distractions: Transformer-based distractor generation and multiple choice question filtering
Jeroen Offerijns, Suzan Verberne, and Tessa Verhoef. 2020 · 2010
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Automatic gap-fill question generation from text books
Manish Agarwal and Prashanth Mannem. 2011 · 2011
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A comparison of greedy and optimal assessment of natural language student input using word-to-word similarity metrics
Vasile Rus and Mihai Lintean. 2012 · 2012
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Probase: A probabilistic taxonomy for text understanding
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A two level model for context sensitive inference rules
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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MCTest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher J.C. Burges, and Erin Renshaw. 2013 · 2013
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Discriminative approach to fill-in-the-blank quiz generation for language learners
Keisuke Sakaguchi, Yuki Arase, and Mamoru Komachi. 2013 · 2013
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Bootstrapping dialog systems with word embeddings
Gabriel Forgues, Joelle Pineau, Jean-Marie Larchevêque, and Réal Tremblay. 2014 · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Automatic generation of challenging distractors using context-sensitive inference rules
Torsten Zesch and Oren Melamud. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 2015 · 2015
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RevUP: Automatic gap-fill question generation from educational texts
Girish Kumar, Rafael Banchs, and Luis Fernando D’Haro. 2015 · 2015
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A hierarchical neural autoencoder for paragraphs and documents
Jiwei Li, Thang Luong, and Dan Jurafsky. 2015 · 2015
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Towards universal paraphrastic sentence embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2015 · 2015
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Questimator: generating knowledge assessments for arbitrary topics
Qi Guo, Chinmay Kulkarni, Aniket Kittur, Jeffrey P Bigham, and Emma Brunskill. 2016 · 2016
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The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016 · 2016
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Automatic generation of context-based fill-in-the-blank exercises using co-occurrence likelihoods and Google n-grams
Jennifer Hill and Rahul Simha. 2016 · 2016
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Who did what: A large-scale person-centered cloze dataset
Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2016 · 2016
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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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Movieqa: Understanding stories in movies through question-answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. 2016 · 2016
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Visual7w: Grounded question answering in images
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. 2016 · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Distractor generation for Chinese fill-in-the-blank items
Shu Jiang and John Lee. 2017 · 2017
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Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension
Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
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Distractor generation with generative adversarial nets for automatically creating fill-in-the-blank questions
Chen Liang, Xiao Yang, Drew Wham, Bart Pursel, Rebecca Passonneaur, and C Lee Giles. 2017 · 2017
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Coarse-to-fine attention models for document summarization
Jeffrey Ling and Alexander Rush. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Multiple choice question generation utilizing an ontology
Katherine Stasaski and Marti A. Hearst. 2017 · 2017
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From neural sentence summarization to headline generation: A coarse-to-fine approach
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017 · 2017
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Automatic multiple choice question generation from text: A survey
Dhawaleswar Rao Ch and Sujan Kumar Saha. 2018 · 2018
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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Automatic distractor suggestion for multiple-choice tests using concept embeddings and information retrieval
Le An Ha and Victoria Yaneva. 2018 · 2018
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WorldTree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference
Peter Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton Morrison. 2018 · 2018
Cited alongside, same era.
Dvqa: Understanding data visualizations via question answering
Kushal Kafle, Brian Price, Scott Cohen, and Christopher Kanan. 2018 · 2018
Cited alongside, same era.
Distractor generation for multiple choice questions using Learning to Rank
Chen Liang, Xiao Yang, Neisarg Dave, Drew Wham, Bart Pursel, and C. Lee Giles. 2018 · 2018
Cited alongside, same era.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
Cited alongside, same era.
Diverse distractor generation for constructing high-quality multiple choice questions
Jiayuan Xie, Ningxin Peng, Yi Cai, Tao Wang, and Qingbao Huang. 2021 · 2021
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A review on question generation from natural language text
Ruqing Zhang, Jiafeng Guo, Lu Chen, Yixing Fan, and Xueqi Cheng. 2021 · 2021
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Cont: Contrastive neural text generation
Chenxin An, Jiangtao Feng, Kai Lv, Lingpeng Kong, Xipeng Qiu, and Xuanjing Huang. 2022 · 2022
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Learning to reuse distractors to support multiple choice question generation in education
Semere Kiros Bitew, Amir Hadifar, Lucas Sterckx, Johannes Deleu, Chris Develder, and Thomas Demeester. 2022 · 2022
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UniGeo: Unifying geometry logical reasoning via reformulating mathematical expression
Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen, and Xiaodan Liang. 2022a · 2022
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Using a common sense knowledge base to auto generate multi-dimensional vocabulary assessments
Ruhi Sharma Mittal, Seema Nagar, Mourvi Sharma, Utkarsh Dwivedi, Prasenjit Dey, and Ravi Kokku. 2018 · 2018
Cited alongside, same era.
Automatic distractor generation for multiple-choice english vocabulary questions
Yuni Susanti, Takenobu Tokunaga, Hitoshi Nishikawa, and Hiroyuki Obari. 2018 · 2018
Cited alongside, same era.
Large-scale cloze test dataset created by teachers
Qizhe Xie, Guokun Lai, Zihang Dai, and Eduard Hovy. 2018 · 2018
Cited alongside, same era.
RecipeQA: A challenge dataset for multimodal comprehension of cooking recipes
Semih Yagcioglu, Aykut Erdem, Erkut Erdem, and Nazli Ikizler-Cinbis. 2018 · 2018
Cited alongside, same era.
MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
Cited alongside, same era.
SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Cited alongside, same era.
Language gans falling short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin. 2019 · 2019
Cited alongside, same era.
CDGP: Automatic cloze distractor generation based on pre-trained language model
Shang-Hsuan Chiang, Ssu-Cheng Wang, and Yao-Chung Fan. 2022 · 2022
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A survey of natural language generation
Chenhe Dong, Yinghui Li, Haifan Gong, et al. 2022 · 2022
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Word2course: creating interactive courses from as little as a keyword
Sébastien Foucher, Damian Pascual, Oliver Richter, and Roger Wattenhofer. 2022 · 2022
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FrenchMedMCQA: A French multiple-choice question answering dataset for medical domain
Yanis Labrak, Adrien Bazoge, Richard Dufour, Beatrice Daille, Pierre-Antoine Gourraud, Emmanuel Morin, and Mickael Rouvier. 2022 · 2022
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Evaluating the knowledge dependency of questions
Hyeongdon Moon, Yoonseok Yang, Hangyeol Yu, Seunghyun Lee, Myeongho Jeong, Juneyoung Park, Jamin Shin, Minsam Kim, and Seungtaek Choi. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022 · 2022
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Automatic generation of distractors for fill-in-the-blank exercises with round-trip neural machine translation
Subhadarshi Panda, Frank Palma Gomez, Michael Flor, and Alla Rozovskaya. 2022 · 2022
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End-to-end generation of multiple-choice questions using text-to-text transfer transformer models
Ricardo Rodriguez-Torrealba, Eva Garcia-Lopez, and Antonio Garcia-Cabot. 2022 · 2022
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A survey of evaluation metrics used for nlg systems
Ananya B Sai, Akash Kumar Mohankumar, and Mitesh M Khapra. 2022 · 2022
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Leaf: Multiple-choice question generation
Kristiyan Vachev, Momchil Hardalov, Georgi Karadzhov, Georgi Georgiev, Ivan Koychev, and Preslav Nakov. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Closing the gap: Automated distractor generation in japanese language testing
Tim Andersson and Pablo Picazo-Sanchez. 2023 · 2023
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Semere Kiros Bitew, Johannes Deleu, Chris Develder, and Thomas Demeester. 2023 · 2023
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Bilal Ghanem and Alona Fyshe. 2023 · 2023
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Eduqg: A multi-format multiple-choice dataset for the educational domain
Amir Hadifar, Semere Kiros Bitew, Johannes Deleu, Chris Develder, and Thomas Demeester. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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A novel approach to generate distractors for multiple choice questions
Archana Praveen Kumar, Ashalatha Nayak, Manjula Shenoy, Shashank Goyal, et al. 2023 · 2023
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Adversarial text generation by search and learning
Guoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, and Honglei Lyu. 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. 2023 · 2023
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023 · 2023
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Assessing the quality of multiple-choice questions using gpt-4 and rule-based methods
Steven Moore, Huy A Nguyen, Tianying Chen, and John Stamper. 2023 · 2023
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Using neural machine translation for generating diverse challenging exercises for language learner
Frank Palma Gomez, Subhadarshi Panda, Michael Flor, and Alla Rozovskaya. 2023 · 2023
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Assessing distractors in multiple-choice tests
Vatsal Raina, Adian Liusie, and Mark Gales. 2023 · 2023
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Qdg: A unified model for automatic question-distractor pairs generation
Pengju Shuai, Li Li, Sishun Liu, and Jun Shen. 2023 · 2023
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Generating multiple choice questions for computing courses using large language models
Andrew Tran, Kenneth Angelikas, Egi Rama, Chiku Okechukwu, David H Smith, and Stephen MacNeil. 2023 · 2023
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Distractor generation based on Text2Text language models with pseudo Kullback-Leibler divergence regulation
Hui-Juan Wang, Kai-Yu Hsieh, Han-Cheng Yu, Jui-Ching Tsou, Yu An Shih, Chen-Hua Huang, and Yao-Chung Fan. 2023a · 2023
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Distractor generation for fill-in-the-blank exercises by question type
Nana Yoshimi, Tomoyuki Kajiwara, Satoru Uchida, Yuki Arase, and Takashi Ninomiya. 2023 · 2023
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Cloze quality estimation for language assessment
Zizheng Zhang, Masato Mita, and Mamoru Komachi. 2023b · 2023
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Automated distractor generation for fill-in-the-blank items using a prompt-based learning approach
Jiyun Zu, Ikkyu Choi, and Jiangang Hao. 2023 · 2023
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Distractor generation through text-to-text transformer models
David De-Fitero-Dominguez, Eva Garcia-Lopez, Antonio Garcia-Cabot, Jesus-Angel Del-Hoyo-Gabaldon, and Antonio Moreno-Cediel. 2024 · 2024
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Can we learn question, answer, and distractors all from an image? a new task for multiple-choice visual question answering
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A comparative study of ai-generated (gpt-4) and human-crafted mcqs in programming education
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Beyond the obvious multi-choice options: Introducing a toolkit for distractor generation enhanced with nli filtering
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Automatic distractor generation for multiple choice questions in standard tests
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