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Large Language Models (LLMs) such as ChatGPT have demonstrated remarkable performance across various tasks and have garnered significant attention from both researchers and practitioners.
Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38
1995
Earlier work this paper cites.
Ramsden, P.: Learning to teach in higher education. Routledge (2003)
2003
Earlier work this paper cites.
Roediger III, H.L., Karpicke, J.D.: Test-enhanced learning: Taking memory tests improves long-term retention. Psychological science 17
2006
Earlier work this paper cites.
Sakai, T.: Evaluating information retrieval metrics based on bootstrap hypothesis tests. IPSJ Digital Courier 3
2007
Earlier work this paper cites.
Papasalouros, A., Kanaris, K., Kotis, K.: Automatic generation of multiple choice questions from domain ontologies. e-Learning 1
2008
Earlier work this paper cites.
Pino, J., Heilman, M., Eskenazi, M.: A selection strategy to improve cloze question quality. In: Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains. 9th International Conference on Intelligent Tutoring Systems, Montreal, Canada. pp. 22–32. Citeseer (2008)
2008
Earlier work this paper cites.
Mitkov, R., Varga, A., Rello, L., et al.: Semantic similarity of distractors in multiple-choice tests: extrinsic evaluation. In: Proceedings of the workshop on geometrical models of natural language semantics. pp. 49–56 (2009)
2009
Earlier work this paper cites.
McHugh, M.L.: Interrater reliability: the kappa statistic. Biochemia medica 22
2012
Earlier work this paper cites.
Alsubait, T., Parsia, B., Sattler, U.: Generating multiple questions from ontologies: How far can we go? In: Proceedings from the First International Workshop on Educational Knowledge Management (EKM 2014), Linköping, November 24, 2014. pp. 19–30. No. 104, Linköping University Electronic Press (2014)
2014
Earlier work this paper cites.
Guo, Q., Kulkarni, C., Kittur, A., Bigham, J.P., Brunskill, E.: Questimator: Generating knowledge assessments for arbitrary topics. In: IJCAI-16: Proceedings of the AAAI Twenty-Fifth International Joint Conference on Artificial Intelligence (2016)
2016
Earlier work this paper cites.
Gierl, M.J., Bulut, O., Guo, Q., Zhang, X.: Developing, analyzing, and using distractors for multiple-choice tests in education: A comprehensive review. Review of Educational Research 87
2017
Earlier work this paper cites.
Jiang, S., Lee, J.S.: Distractor generation for chinese fill-in-the-blank items. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. pp. 143–148 (2017)
2017
Earlier work this paper cites.
Faizan, A., Lohmann, S.: Automatic generation of multiple choice questions from slide content using linked data. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. pp. 1–8 (2018)
2018
Earlier work this paper cites.
Liang, C., Yang, X., Dave, N., Wham, D., Pursel, B., Giles, C.L.: Distractor generation for multiple choice questions using learning to rank. In: Proceedings of the thirteenth workshop on innovative use of NLP for building educational applications. pp. 284–290 (2018)
2018
Cited alongside, same era.
Gao, Y., Bing, L., Li, P., King, I., Lyu, M.R.: Generating distractors for reading comprehension questions from real examinations. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 6423–6430 (2019)
2019
Cited alongside, same era.
Leo, J., Kurdi, G., Matentzoglu, N., Parsia, B., Sattler, U., Forge, S., Donato, G., Dowling, W.: Ontology-based generation of medical, multi-term mcqs. International Journal of Artificial Intelligence in Education 29
2019
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Cited alongside, same era.
Bitew, S.K., Hadifar, A., Sterckx, L., Deleu, J., Develder, C., Demeester, T.: Learning to reuse distractors to support multiple choice question generation in education. IEEE Transactions on Learning Technologies (2022). https://doi.org/10.1109/TLT.2022.3226523
2022
Later among the works it cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35
2022
Later among the works it cites.
Panda, S., Palma Gomez, F., Flor, M., Rozovskaya, A.: Automatic generation of distractors for fill-in-the-blank exercises with round-trip neural machine translation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. pp. 391–401. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.acl-srw.31
2022
Later among the works it cites.
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Yeung, C.Y., Lee, J.S., Tsou, B.K.: Difficulty-aware distractor generation for gap-fill items. In: Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association. pp. 159–164 (2019)
2019
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Cited alongside, same era.
Chung, H.L., Chan, Y.H., Fan, Y.C.: A BERT-based distractor generation scheme with multi-tasking and negative answer training strategies. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 4390–4400. Association for Computational Linguistics, Online (Nov 2020). https://doi.org/10.18653/v1/2020.findings-emnlp.393
2020
Cited alongside, same era.
Kurdi, G., Leo, J., Parsia, B., Sattler, U., Al-Emari, S.: A systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education 30
2020
Cited alongside, same era.
Zhou, X., Luo, S., Wu, Y.: Co-attention hierarchical network: Generating coherent long distractors for reading comprehension. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 9725–9732 (2020)
2020
Cited alongside, same era.
Cavalcanti, A.P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.S., Gašević, D., Mello, R.F.: Automatic feedback in online learning environments: A systematic literature review. Computers and Education: Artificial Intelligence 2
2021
Cited alongside, same era.
Kalpakchi, D., Boye, J.: BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset. In: Proceedings of the 14th International Conference on Natural Language Generation. pp. 387–403. Association for Computational Linguistics, Aberdeen, Scotland, UK (Aug 2021), https://aclanthology.org/2021.inlg-1.43
2021
Cited alongside, same era.
Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., Raffel, C.: mT5: A massively multilingual pre-trained text-to-text transformer. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 483–498. Association for Computational Linguistics, Online (Jun 2021). https://doi.org/10.18653/v1/2021.naacl-main.41
2021
Cited alongside, same era.
Ramesh, D., Sanampudi, S.K.: An automated essay scoring systems: a systematic literature review. Artificial Intelligence Review 55
2022
Later among the works it cites.
Rodriguez-Torrealba, R., Garcia-Lopez, E., Garcia-Cabot, A.: End-to-end generation of multiple-choice questions using text-to-text transfer transformer models. Expert Systems with Applications 208
2022
Later among the works it cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., brian ichter, Xia, F., Chi, E.H., Le, Q.V., Zhou, D.: Chain of thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems (2022), https://openreview.net/forum?id=˙VjQlMeSB˙J
2022
Later among the works it cites.
Bitew, S.K., Deleu, J., Dogruöz, A.S., Develder, C., Demeester, T.: Learning from partially annotated data: Example-aware creation of gap-filling exercises for language learning. In: Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023). pp. 598–609. Association for Computational Linguistics, Toronto, Canada (Jul 2023), https://aclanthology.org/2023.bea-1.51
2023
Closest in time.
Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023)
2023
Closest in time.
Gilson, A., Safranek, C.W., Huang, T., Socrates, V., Chi, L., Taylor, R.A., Chartash, D., et al.: How does chatgpt perform on the united states medical licensing examination? the implications of large language models for medical education and knowledge assessment. JMIR Medical Education 9
2023
Closest in time.
Li, Y., Sha, L., Yan, L., Lin, J., Raković, M., Galbraith, K., Lyons, K., Gašević, D., Chen, G.: Can large language models write reflectively. Computers and Education: Artificial Intelligence 4
2023
Closest in time.
OpenAI: Gpt-4 technical report (2023)
2023
Closest in time.