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The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.
Large language models in medicine
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PISA Programme for international student assessment (PISA) PISA 2000 technical report: PISA 2000 technical report
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Educational data mining: A survey from 1995 to 2005
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Educational data mining: a review of the state of the art
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The principles for building the “international corpus of learner chinese”
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A new dataset and method for automatically grading ESOL texts. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies . 180–189
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TOEFL11: A corpus of non-native English
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Data mining in education
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The wiked error corpus: A corpus of corrective wikipedia edits and its application to grammatical error correction. In Advances in Natural Language Processing: 9th International Conference on NLP, PolTAL 2014, Warsaw, Poland, September 17-19, 2014. Proceedings 9 . Springer, 478–490
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Defects4J: A database of existing faults to enable controlled testing studies for Java programs. In Proceedings of the 2014 international symposium on software testing and analysis . 437–440
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The CoNLL-2014 shared task on grammatical error correction. In Proceedings of the eighteenth conference on computational natural language learning: shared task . 1–14
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Annotating argument components and relations in persuasive essays. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: Technical papers . 1501–1510
Christian Stab and Iryna Gurevych. 2014 · 2014
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GutenTag: an NLP-driven tool for digital humanities research in the Project Gutenberg corpus. In Proceedings of the Fourth Workshop on Computational Linguistics for Literature . 42–47
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Data mining and education
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The ManyBugs and IntroClass benchmarks for automated repair of C programs
Claire Le Goues, Neal Holtschulte, Edward K Smith, Yuriy Brun, Premkumar Devanbu, Stephanie Forrest, and Westley Weimer. 2015 · 2015
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Introduction to SIGHAN 2015 bake-off for Chinese spelling check. In Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing . 32–37
Yuen-Hsien Tseng, Lung-Hao Lee, Li-Ping Chang, and Hsin-Hsi Chen. 2015 · 2015
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Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS
Rianne Conijn, Chris Snijders, Ad Kleingeld, and Uwe Matzat. 2016 · 2016
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How well do computers solve math word problems? large-scale dataset construction and evaluation. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 887–896
Danqing Huang, Shuming Shi, Chin-Yew Lin, Jian Yin, and Wei-Ying Ma. 2016 · 2016
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A diagram is worth a dozen images. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14 . Springer, 235–251
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. 2016 · 2016
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Annotating derivations: A new evaluation strategy and dataset for algebra word problems
Shyam Upadhyay and Ming-Wei Chang. 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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QuixBugs: A multi-lingual program repair benchmark set based on the Quixey Challenge. In Proceedings Companion of the 2017 ACM SIGPLAN international conference on systems, programming, languages, and applications: software for humanity . 55–56
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017 · 2017
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Investigating the effect of an adaptive learning intervention on students’ learning
Min Liu, Emily McKelroy, Stephanie B Corliss, and Jamison Carrigan. 2017 · 2017
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Examining national trends in educational placements for students with significant disabilities
Mary E Morningstar, Jennifer A Kurth, and Paul E Johnson. 2017 · 2017
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Deep neural solver for math word problems. In Proceedings of the 2017 conference on empirical methods in natural language processing . 845–854
Yan Wang, Xiaojiang Liu, and Shuming Shi. 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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LearningQ: a large-scale dataset for educational question generation. In Proceedings of the international AAAI conference on web and social media , Vol. 12
Guanliang Chen, Jie Yang, Claudia Hauff, and Geert-Jan Houben. 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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Daesik Kim, Seonhoon Kim, and Nojun Kwak. 2018 · 2018
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MACHINE LEARNING IN EDUCATION-A SURVEY OF CURRENT RESEARCH TRENDS
Danijel Kučak, Vedran Juričić, and Goran Đambić. 2018 · 2018
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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 . 284–290
Chen Liang, Xiao Yang, Neisarg Dave, Drew Wham, Bart Pursel, and C Lee Giles. 2018 · 2018
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Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Peter Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
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The BEA-2019 shared task on grammatical error correction. In Proceedings of the fourteenth workshop on innovative use of NLP for building educational applications . 52–75
Christopher Bryant, Mariano Felice, Øistein E Andersen, and Ted Briscoe. 2019 · 2019
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A Survey of the General Public’s Views on the Ethics of Using AI in Education. In Artificial Intelligence in Education (Lecture Notes in Computer Science, Vol. 11625) , S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, and R. Luckin (Eds.). Springer, Cham
A. Latham and S. Goltz. 2019 · 2019
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Automatic short answer grading via multiway attention networks. In Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II 20 . Springer, 169–173
Tiaoqiao Liu, Wenbiao Ding, Zhiwei Wang, Jiliang Tang, Gale Yan Huang, and Zitao Liu. 2019 · 2019
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Deep learning recommendation model for personalization and recommendation systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G Azzolini, et al · 2019
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Grammar error correction in morphologically rich languages: The case of Russian
Alla Rozovskaya and Dan Roth. 2019 · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2019 · 2019
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Artificial intelligence in education: A review
Lijia Chen, Pingping Chen, and Zhijian Lin. 2020 · 2020
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Demographic predictors of students’ science participation over the age of 16: An Australian case study
Grant Cooper, Amanda Berry, and James Baglin. 2020 · 2020
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Neural grammatical error correction for romanian. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) . IEEE, 625–631
Teodor-Mihai Cotet, Stefan Ruseti, and Mihai Dascalu. 2020 · 2020
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Developing NLP tools with a new corpus of learner Spanish. In Proceedings of the Twelfth Language Resources and Evaluation Conference . 7238–7243
Sam Davidson, Aaron Yamada, Paloma Fernandez Mira, Agustina Carando, Claudia H Sanchez Gutierrez, and Kenji Sagae. 2020 · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2020
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Using the Concept of Game-Based Learning in Education
Zi-Yu Liu, Zaffar Ahmed Shaikh, and Farida Gazizova. 2020 · 2020
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Educational data mining and learning analytics: An updated survey
Cristobal Romero and Sebastian Ventura. 2020 · 2020
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The Tatoeba Translation Challenge–Realistic Data Sets for Low Resource and Multilingual MT
Jörg Tiedemann. 2020 · 2020
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Automatic generation of headlines for online math questions. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 9490–9497
Ke Yuan, Dafang He, Zhuoren Jiang, Liangcai Gao, Zhi Tang, and C Lee Giles. 2020 · 2020
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Ape210k: A large-scale and template-rich dataset of math word problems
Wei Zhao, Mingyue Shang, Yang Liu, Liang Wang, and Jingming Liu. 2020 · 2020
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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
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021 · 2021
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Inter-GPS: Interpretable geometry problem solving with formal language and symbolic reasoning
Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, and Song-Chun Zhu. 2021a · 2021
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Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning
Pan Lu, Liang Qiu, Jiaqi Chen, Tony Xia, Yizhou Zhao, Wei Zhang, Zhou Yu, Xiaodan Liang, and Song-Chun Zhu. 2021b · 2021
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Personalized education in the artificial intelligence era: what to expect next
Setareh Maghsudi, Andrew Lan, Jie Xu, and Mihaela van Der Schaar. 2021 · 2021
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A diverse corpus for evaluating and developing English math word problem solvers
Shen-Yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2021 · 2021
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A simple recipe for multilingual grammatical error correction
Sascha Rothe, Jonathan Mallinson, Eric Malmi, Sebastian Krause, and Aliaksei Severyn. 2021 · 2021
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UA-GEC: Grammatical error correction and fluency corpus for the ukrainian language
Oleksiy Syvokon and Olena Nahorna. 2021 · 2021
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Ethical and social risks of harm from Language Models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, Zac Kenton, Sasha Brown, Will Hawkins, Tom Stepleton, Courtney Biles, Abeba Birhane, Julia Haas, Laura Rimell, Lisa Anne Hendricks, William Isaac, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2021 · 2021
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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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Michael Bommarito II and Daniel Martin Katz. 2022 · 2022
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2022 · 2022
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Experts’ view on challenges and needs for fairness in artificial intelligence for education. In International Conference on Artificial Intelligence in Education . Springer, 243–255
Gianni Fenu, Roberta Galici, and Mirko Marras. 2022 · 2022
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Khanq: A dataset for generating deep questions in education. In Proceedings of the 29th International Conference on Computational Linguistics . 5925–5938
Huanli Gong, Liangming Pan, and Hengchang Hu. 2022 · 2022
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Automating code review activities by large-scale pre-training. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1035–1047
Zhiyu Li, Shuai Lu, Daya Guo, Nan Duan, Shailesh Jannu, Grant Jenks, Deep Majumder, Jared Green, Alexey Svyatkovskiy, Shengyu Fu, et al · 2022
Cited alongside, same era.
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. 2022 · 2022
Cited alongside, same era.
Czech grammar error correction with a large and diverse corpus
Jakub Náplava, Milan Straka, Jana Straková, and Alexandr Rosen. 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, et al · 2022
Cited alongside, same era.
Thrilled by your progress! Large language models (GPT-4) no longer struggle to pass assessments in higher education programming courses. In Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1 . 78–92
Jaromir Savelka, Arav Agarwal, Marshall An, Chris Bogart, and Majd Sakr. 2023 · 2023
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Mohamed R. Shoaib, Zefan Wang, Milad Taleby Ahvanooey, and Jun Zhao. 2023 · 2023
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Deduction under Perturbed Evidence: Probing Student Simulation (Knowledge Tracing) Capabilities of Large Language Models
Shashank Sonkar and Richard G Baraniuk. 2023 · 2023
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Offline prompt evaluation and optimization with inverse reinforcement learning
Hao Sun. 2023 · 2023
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Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering. In Conference on health, inference, and learning . PMLR, 248–260
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022 · 2022
Cited alongside, same era.
Large scale analytics of global and regional MOOC providers: Differences in learners’ demographics, preferences, and perceptions
José A Ruipérez-Valiente, Thomas Staubitz, Matt Jenner, Sherif Halawa, Jiayin Zhang, Ignacio Despujol, Jorge Maldonado-Mahauad, German Montoro, Melanie Peffer, Tobias Rohloff, et al · 2022
Cited alongside, same era.
ChatGPT: The end of online exam integrity?
Teo Susnjak. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
FCGEC: fine-grained corpus for Chinese grammatical error correction
Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Jiayu Fu, and Ming Cai. 2022b · 2022
Cited alongside, same era.
Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Jia-Jun Li, Nora Bradford, Branda Sun, et al · 2022
Cited alongside, same era.
Repairing bugs in python assignments using large language models
Jialu Zhang, José Cambronero, Sumit Gulwani, Vu Le, Ruzica Piskac, Gustavo Soares, and Gust Verbruggen. 2022 · 2022
Cited alongside, same era.
Overview of ctc 2021: Chinese text correction for native speakers
Honghong Zhao, Baoxin Wang, Dayong Wu, Wanxiang Che, Zhigang Chen, and Shijin Wang. 2022 · 2022
Cited alongside, same era.
Kehui Tan, Tianqi Pang, and Chenyou Fan. 2023 · 2023
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Analyzing the quality of submissions in online programming courses. In 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) . IEEE, 271–282
Maria Tigina, Anastasiia Birillo, Yaroslav Golubev, Hieke Keuning, Nikolay Vyahhi, and Timofey Bryksin. 2023 · 2023
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Large language models are implicitly topic models: Explaining and finding good demonstrations for in-context learning. In Workshop on Efficient Systems for Foundation Models@ ICML2023
Xinyi Wang, Wanrong Zhu, Michael Saxon, Mark Steyvers, and William Yang Wang. 2023 · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023a · 2023
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Next-gpt: Any-to-any multimodal llm
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. 2023b · 2023
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Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023c · 2023
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An empirical study on challenging math problem solving with gpt-4
Yiran Wu, Feiran Jia, Shaokun Zhang, Qingyun Wu, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, and Chi Wang. 2023d · 2023
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Evaluating reading comprehension exercises generated by LLMs: A showcase of ChatGPT in education applications. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) . 610–625
Changrong Xiao, Sean Xin Xu, Kunpeng Zhang, Yufang Wang, and Lei Xia. 2023 · 2023
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Superclue: A comprehensive chinese large language model benchmark
Liang Xu, Anqi Li, Lei Zhu, Hang Xue, Changtai Zhu, Kangkang Zhao, Haonan He, Xuanwei Zhang, Qiyue Kang, and Zhenzhong Lan. 2023 · 2023
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Gautam Yadav, Ying-Jui Tseng, and Xiaolin Ni. 2023 · 2023
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Rating short l2 essays on the cefr scale with gpt-4. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) . 576–584
Kevin P Yancey, Geoffrey Laflair, Anthony Verardi, and Jill Burstein. 2023 · 2023
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Fingpt: Open-source financial large language models
Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023 · 2023
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How well do Large Language Models perform in Arithmetic tasks?
Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, and Songfang Huang. 2023 · 2023
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Large language models for robotics: A survey
Fanlong Zeng, Wensheng Gan, Yongheng Wang, Ning Liu, and Philip S Yu. 2023 · 2023
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A systematic review of ChatGPT use in K-12 education
Peng Zhang and Gemma Tur. 2023 · 2023
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Does Correction Remain An Problem For Large Language Models?
Xiaowu Zhang, Xiaotian Zhang, Cheng Yang, Hang Yan, and Xipeng Qiu. 2023b · 2023
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Agieval: A human-centric benchmark for evaluating foundation models
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. 2023 · 2023
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Learning by Analogy: Diverse Questions Generation in Math Word Problem. In Findings of the Association for Computational Linguistics: ACL 2023 . 11091–11104
Zihao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei Huang, and Kaizhu Huang. 2023 · 2023
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Red teaming chatgpt via jailbreaking: Bias, robustness, reliability and toxicity
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023 · 2023
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Vox Populi, Vox ChatGPT: Large Language Models, Education and Democracy
Niina Zuber and Jan Gogoll. 2023 · 2023
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QuestionWell
2024 · 2024
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Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations
Hasan Abu-Rasheed, Christian Weber, and Madjid Fathi. 2024 · 2024
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Dated Data: Tracing Knowledge Cutoffs in Large Language Models
Jeffrey Cheng, Marc Marone, Orion Weller, Dawn Lawrie, Daniel Khashabi, and Benjamin Van Durme. 2024 · 2024
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Few-Shot Fairness: Unveiling LLM’s Potential for Fairness-Aware Classification
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