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Within the context of reading comprehension, the task of Distractor Generation (DG) aims to generate several incorrect options to confuse readers.
Improving machine reading comprehension with single-choice decision and transfer learning
Yufan Jiang, Shuangzhi Wu, Jing Gong, Yahui Cheng, Peng Meng, Weiliang Lin, Zhibo Chen, and Mu Li. 2020 · 2011
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
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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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Generating distractors for reading comprehension questions from real examinations
Yifan Gao, Lidong Bing, Piji Li, Irwin King, and Michael R. Lyu. 2019 · 2019
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DREAM: A challenge data set and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie. 2019 · 2019
Earlier work this paper cites.
Learning to distract: A hierarchical multi-decoder network for automated generation of long distractors for multiple-choice questions for reading comprehension
Kaushal Kumar Maurya and Maunendra Sankar Desarkar. 2020 · 2020
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Co-attention hierarchical network: Generating coherent long distractors for reading comprehension
Xiaorui Zhou, Senlin Luo, and Yunfang Wu. 2020 · 2020
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Knowledge-driven distractor generation for cloze-style multiple choice questions
Siyu Ren and Kenny Q. Zhu. 2021 · 2021
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Want to reduce labeling cost? GPT-3 can help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021 · 2021
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Ask me anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré. 2022 · 2022
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Inpars: Data augmentation for information retrieval using large language models
Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. 2022 · 2022
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
Cited alongside, same era.
Investigating relationships between accuracy and diversity in multi-reference text generation
Weike Fang and Meng Jiang. 2022 · 2022
Cited alongside, same era.
Language models in the loop: Incorporating prompting into weak supervision
Ryan Smith, Jason A Fries, Braden Hancock, and Stephen H Bach. 2022 · 2022
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. 2022 · 2022
Using artificial intelligence to create biology multiple choice questions for higher education
NEA Nasution. 2023 · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
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Accurate, diverse and multiple distractor generation with mixture of experts
Fanyi Qu, Che Wang, and Yunfang Wu. 2023 · 2023
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Udapdr: Unsupervised domain adaptation via llm prompting and distillation of rerankers
Jon Saad-Falcon, Omar Khattab, Keshav Santhanam, Radu Florian, Martin Franz, Salim Roukos, Avirup Sil, Md Arafat Sultan, and Christopher Potts. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Chatgpt outperforms crowd-workers for textannotation tasks
F Gilardi, M Alizadeh, and M Kubli. 2023 · 2023
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Annollm: Making large language models to be better crowdsourced annotators
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Contrastive decoding: Open-ended text generation as optimization
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Cited alongside, same era.
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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. 2023 · 2023
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Llm-powered data augmentation for enhanced crosslingual performance
Chenxi Whitehouse, Monojit Choudhury, and Alham Fikri Aji. 2023 · 2023
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A comparative study of ai-generated (gpt-4) and human-crafted mcqs in programming education
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Automatic distractor generation for multiple choice questions in standard tests
Zhaopeng Qiu, Xian Wu, and Wei Fan. 2020 · 2096
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