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Due to the remarkable language understanding and generation abilities of large language models (LLMs), their use in educational applications has been explored.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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Focus on formative feedback
Valerie J Shute. 2008 · 2008
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Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017 · 2017
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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
Earlier work this paper cites.
E-learning critical success factors during the COVID-19 pandemic: A comprehensive analysis of e-learning managerial perspectives
Ammar Y Alqahtani and Albraa A Rajkhan. 2020 · 2020
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Four principles of explainable artificial intelligence
P Jonathon Phillips, Carina A Hahn, Peter C Fontana, David A Broniatowski, and Mark A Przybocki. 2020 · 2020
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Understanding the challenges of e-learning during the global pandemic emergency: The students’ perspective
Mohsin Abdur Rehman, Saira Hanif Soroya, Zuhair Abbas, Farhan Mirza, and Khalid Mahmood. 2021 · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Generate & rank: A multi-task framework for math word problems
Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, and Qun Liu. 2021 · 2021
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Improving math word problems with pre-trained knowledge and hierarchical reasoning
Weijiang Yu, Yingpeng Wen, Fudan Zheng, and Nong Xiao. 2021 · 2021
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Exploring the MIT mathematics and eecs curriculum using large language models
Sarah J Zhang, Samuel Florin, Ariel N Lee, Eamon Niknafs, Andrei Marginean, Annie Wang, Keith Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando Solar-Lezama, and Iddo Drori. 2023 · 2021
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Machine learning based feedback on textual student answers in large courses
Jan Philip Bernius, Stephan Krusche, and Bernd Bruegge. 2022 · 2022
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PAL: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2022 · 2022
Cited alongside, same era.
State-of-the-art generalisation research in NLP: a taxonomy and review
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella Sinclair, Dennis Ulmer, Florian Schottmann, Khuyagbaatar Batsuren, Kaiser Sun, Koustuv Sinha, Leila Khalatbari, Maria Ryskina, Rita Frieske, Ryan Cotterell, and Zhijing Jin. 2022 · 2022
Cited alongside, same era.
Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 2022
Cited alongside, same era.
Assessing the challenges of e-learning in Malaysia during the pandemic of COVID-19 using the geo-spatial approach
Adi Jafar, Ramli Dollah, Nordin Sakke, Mohammad Tahir Mapa, Ang Kean Hua, Oliver Valentine Eboy, Eko Prayitno Joko, Diana Hassan, and Chong Vun Hung. 2022 · 2022
ChatGPT for good? On opportunities and challenges of large language models for education
Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, Stepha Krusche, Gitta Kutyniok, Tilman Michaeli, Claudia Nerdel, Jürgen Pfeffer, Oleksandra Poquet, Michael Sailer, Albrecht Schmidt, Tina Seidel, Matthias Stadler, Jochen Weller, Jochen Kuhn, and Gjergji Kasneci. 2023 · 2023
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A systematic study and comprehensive evaluation of chatgpt on benchmark datasets
Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, and Jimmy Xiangji Huang. 2023 · 2023
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Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 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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Cited alongside, same era.
Learning to reason deductively: Math word problem solving as complex relation extraction
Zhanming Jie, Jierui Li, and Wei Lu. 2022 · 2022
Cited alongside, same era.
Mind’s Eye: Grounded language model reasoning through simulation
Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, and Andrew M Dai. 2022 · 2022
Cited alongside, same era.
Assessing the quality of student-generated short answer questions using GPT-3
Steven Moore, Huy A Nguyen, Norman Bier, Tanvi Domadia, and John Stamper. 2022 · 2022
Cited alongside, same era.
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, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Cited alongside, same era.
The AI Teacher Test: Measuring the pedagogical ability of Blender and GPT-3 in educational dialogues
Anaïs Tack and Chris Piech. 2022 · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
A categorical archive of ChatGPT failures
Ali Borji. 2023 · 2023
Cited alongside, same era.
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. 2023 · 2023
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OpenAI. 2023 · 2023
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Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning
Michael Sailer, Elisabeth Bauer, Riikka Hofmann, Jan Kiesewetter, Julia Glas, Iryna Gurevych, and Frank Fischer. 2023 · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Large language models perform diagnostic reasoning
Cheng-Kuang Wu, Wei-Lin Chen, and Hsin-Hsi Chen. 2023 · 2023
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Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
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