Built on

L. Simon, R. Webster, and J. Rabin, “Revisiting precision and recall definition for generative model evaluation,” arXiv preprint arXiv:1905.05441. , 5 2019. [Online]. Available: http://arxiv.org/abs/1905.05441
Original
1905
Earlier work this paper cites.
I. C. Fountas and G. S. Pinnell, Guided reading: Good first teaching for all children. ERIC, 1996
1996
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
O. Roy and M. Vetterli, “The effective rank: A measure of effective dimensionality,” in 2007 15th European signal processing conference . IEEE, 2007, pp. 606–610
2007
Earlier work this paper cites.
T. A. Zucker, L. M. Justice, S. B. Piasta, and J. N. Kaderavek, “Preschool teachers’ literal and inferential questions and children’s responses during whole-class shared reading,” Early Childhood Research Quarterly , vol. 25, pp. 65–83, 3 2010
2010
Earlier work this paper cites.
M. Heilman, “Automatic factual question generation from text,” Ph.D. dissertation, Carnegie Mellon University, 2011
2011
Earlier work this paper cites.
Y. Chali and S. A. Hasan, “Towards automatic topical question generation,” in Proceedings of COLING 2012 , 2012, pp. 475–492
2012
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473. , 9 2014. [Online]. Available: http://arxiv.org/abs/1409.0473
Original
2014
Earlier work this paper cites.
M. P. Ford, Guided reading: What’s new, and what’s next . Capstone, 2015
2015
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
M. Habib, “Assessment of reading comprehension,” Revista Romaneasca pentru Educatie Multidimensionala , vol. 8, pp. 125–147, 2016. [Online]. Available: http://dx.doi.org/10.18662/rrem/2016.0801.08
Original
2016
Earlier work this paper cites.

Similar

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Łukasz Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 2017-Decem, pp. 5999–6009, 2017
2017
Cited alongside, same era.
X. Du, J. Shao, and C. Cardie, “Learning to ask: Neural question generation for reading comprehension,” arXiv preprint arXiv:1705.00106 , 2017
Original
2017
Cited alongside, same era.
N. Duan, D. Tang, P. Chen, and M. Zhou, “Question generation for question answering,” in Proceedings of the 2017 conference on empirical methods in natural language processing , 2017, pp. 866–874
2017
Cited alongside, same era.
S. Degener and J. Berne, “Complex questions promote complex thinking,” Reading Teacher , vol. 70, pp. 595–599, 3 2017
2017
Cited alongside, same era.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems, , vol. 30, 6 2017. [Online]. Available: http://arxiv.org/abs/1706.08500

Then

L. P. Blything, A. Hardie, and K. Cain, “Question asking during reading comprehension instruction: A corpus study of how question type influences the linguistic complexity of primary school students’ responses,” Reading Research Quarterly , vol. 55, pp. 443–472, 7 2020
2020
Later among the works it cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, pp. 139–144, 10 2020
2020
Later among the works it cites.
B. Yao, D. Wang, T. Wu, Z. Zhang, T. J.-J. Li, M. Yu, and Y. Xu, “It is ai’s turn to ask humans a question: Question-answer pair generation for children’s story books,” arXiv preprint arXiv:2109.03423 , 9 2021. [Online]. Available: http://arxiv.org/abs/2109.03423
Original
2021
Later among the works it cites.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y. Tay, N. Shazeer, V. Prabhakaran, E. Reif, N. Du, B. Hutchinson, R. Pope, J. Bradbury, J. Austin, M. Isard, G. Gur-Ari, P. Yin, T. Duke, A. Levskaya, S. Ghemawat, S. Dev, H. Michalewski, X. Garcia, V. Misra, K. Robinson, L. Fedus, D. Zhou, D. Ippolito, D. Luan, H. Lim, B. Zoph, A. Spiridonov, R. Sepassi, D. Dohan, S. Agrawal, M. Omernick, A. M. Dai, T. S. Pillai, M. Pellat, A. Lewkowycz, E. Moreira, R. Child, O. Polozov, K. Lee, Z. Zhou, X. Wang, B. Saeta, M. Diaz, O. Firat, M. Catasta, J. Wei, K. Meier-Hellstern, D. Eck, J. Dean, S. Petrov, and N. Fiedel, “Palm: Scaling language modeling with pathways,” arXiv preprint arXiv:2204.02311 , 4 2022. [Online]. Available: http://arxiv.org/abs/2204.02311

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2023

Are Large Language Models Fit For Guided Reading?

Ochieng, Peter

Understand

This paper looks at the ability of large language models to participate in educational guided reading.

We specifically, evaluate their ability to generate meaningful questions from the input text, generate diverse questions both in terms of content coverage and difficulty of the questions and evaluate their ability to recommend part of the text that a student should re-read based on the student's responses to the questions.
Based on our evaluation of ChatGPT and Bard, we report that, 1) Large language models are able to generate high quality meaningful questions that have high correlation with the input text, 2) They generate diverse question that cover most topics in the input text even though this ability is significantly degraded as the input text increases, 3)The large language models are able to generate both low and high cognitive questions even though they are significantly biased toward low cognitive question, 4) They are able to effectively summarize responses and extract a portion of text that should be re-read.

Built on

L. Simon, R. Webster, and J. Rabin, “Revisiting precision and recall definition for generative model evaluation,” arXiv preprint arXiv:1905.05441. , 5 2019. [Online]. Available: http://arxiv.org/abs/1905.05441
Original
1905
Earlier work this paper cites.
I. C. Fountas and G. S. Pinnell, Guided reading: Good first teaching for all children. ERIC, 1996
1996
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
O. Roy and M. Vetterli, “The effective rank: A measure of effective dimensionality,” in 2007 15th European signal processing conference . IEEE, 2007, pp. 606–610
2007
Earlier work this paper cites.
T. A. Zucker, L. M. Justice, S. B. Piasta, and J. N. Kaderavek, “Preschool teachers’ literal and inferential questions and children’s responses during whole-class shared reading,” Early Childhood Research Quarterly , vol. 25, pp. 65–83, 3 2010
2010
Earlier work this paper cites.
M. Heilman, “Automatic factual question generation from text,” Ph.D. dissertation, Carnegie Mellon University, 2011
2011
Earlier work this paper cites.
Y. Chali and S. A. Hasan, “Towards automatic topical question generation,” in Proceedings of COLING 2012 , 2012, pp. 475–492
2012
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473. , 9 2014. [Online]. Available: http://arxiv.org/abs/1409.0473
Original
2014
Earlier work this paper cites.
M. P. Ford, Guided reading: What’s new, and what’s next . Capstone, 2015
2015
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
M. Habib, “Assessment of reading comprehension,” Revista Romaneasca pentru Educatie Multidimensionala , vol. 8, pp. 125–147, 2016. [Online]. Available: http://dx.doi.org/10.18662/rrem/2016.0801.08
Original
2016
Earlier work this paper cites.

Similar

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Łukasz Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 2017-Decem, pp. 5999–6009, 2017
2017
Cited alongside, same era.
X. Du, J. Shao, and C. Cardie, “Learning to ask: Neural question generation for reading comprehension,” arXiv preprint arXiv:1705.00106 , 2017
Original
2017
Cited alongside, same era.
N. Duan, D. Tang, P. Chen, and M. Zhou, “Question generation for question answering,” in Proceedings of the 2017 conference on empirical methods in natural language processing , 2017, pp. 866–874
2017
Cited alongside, same era.
S. Degener and J. Berne, “Complex questions promote complex thinking,” Reading Teacher , vol. 70, pp. 595–599, 3 2017
2017
Cited alongside, same era.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems, , vol. 30, 6 2017. [Online]. Available: http://arxiv.org/abs/1706.08500

Then

L. P. Blything, A. Hardie, and K. Cain, “Question asking during reading comprehension instruction: A corpus study of how question type influences the linguistic complexity of primary school students’ responses,” Reading Research Quarterly , vol. 55, pp. 443–472, 7 2020
2020
Later among the works it cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, pp. 139–144, 10 2020
2020
Later among the works it cites.
B. Yao, D. Wang, T. Wu, Z. Zhang, T. J.-J. Li, M. Yu, and Y. Xu, “It is ai’s turn to ask humans a question: Question-answer pair generation for children’s story books,” arXiv preprint arXiv:2109.03423 , 9 2021. [Online]. Available: http://arxiv.org/abs/2109.03423
Original
2021
Later among the works it cites.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y. Tay, N. Shazeer, V. Prabhakaran, E. Reif, N. Du, B. Hutchinson, R. Pope, J. Bradbury, J. Austin, M. Isard, G. Gur-Ari, P. Yin, T. Duke, A. Levskaya, S. Ghemawat, S. Dev, H. Michalewski, X. Garcia, V. Misra, K. Robinson, L. Fedus, D. Zhou, D. Ippolito, D. Luan, H. Lim, B. Zoph, A. Spiridonov, R. Sepassi, D. Dohan, S. Agrawal, M. Omernick, A. M. Dai, T. S. Pillai, M. Pellat, A. Lewkowycz, E. Moreira, R. Child, O. Polozov, K. Lee, Z. Zhou, X. Wang, B. Saeta, M. Diaz, O. Firat, M. Catasta, J. Wei, K. Meier-Hellstern, D. Eck, J. Dean, S. Petrov, and N. Fiedel, “Palm: Scaling language modeling with pathways,” arXiv preprint arXiv:2204.02311 , 4 2022. [Online]. Available: http://arxiv.org/abs/2204.02311

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2017

Cited alongside, same era.

Q. Zhou, N. Yang, F. Wei, C. Tan, H. Bao, and M. Zhou, “Neural question generation from text: A preliminary study,” in Natural Language Processing and Chinese Computing: 6th CCF International Conference, NLPCC 2017, Dalian, China, November 8–12, 2017, Proceedings 6 . Springer, 2018, pp. 662–671

2018

Cited alongside, same era.

V. Harrison and M. Walker, “Neural generation of diverse questions using answer focus, contextual and linguistic features,” arXiv preprint arXiv:1809.02637 , 2018

Original

2018

Cited alongside, same era.

M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly, “Assessing generative models via precision and recall,” Advances in neural information processing systems , vol. 31, 2018

2018

Cited alongside, same era.

L. Pan, W. Lei, T.-S. Chua, and M.-Y. Kan, “Recent advances in neural question generation,” arXiv preprint arXiv:1905.08949 , 2019

Original

2019

Cited alongside, same era.

T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “Bertscore: Evaluating text generation with bert,” arXiv preprint arXiv:1904.09675 , 2019

Original

2019

Cited alongside, same era.

T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. Mccandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901., 2020. [Online]. Available: https://commoncrawl.org/the-data/

2020

Cited alongside, same era.

Original

2022

Later among the works it cites.

R. Taylor, M. Kardas, G. Cucurull, T. Scialom, A. Hartshorn, E. Saravia, A. Poulton, V. Kerkez, and R. Stojnic, “Galactica: A large language model for science,” arXiv preprint arXiv:2211.0908 , 11 2022. [Online]. Available: http://arxiv.org/abs/2211.09085

Original

2022

Later among the works it cites.

Z. Zhao, Y. Hou, D. Wang, M. Yu, C. Liu, and X. Ma, “Educational question generation of children storybooks via question type distribution learning and event-centric summarization,” arXiv preprint arXiv:2203.14187 , vol. 1, pp. 5073–5085, 2022

Original

2022

Later among the works it cites.

H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, “Llama: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971. , 2 2023. [Online]. Available: http://arxiv.org/abs/2302.13971

Original

2023

Closest in time.

S. S. Biswas, “Potential use of chat gpt in global warming,” Annals of Biomedical Engineering , 2023

2023

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M. Hasnain, “Chatgpt applications and challenges in controlling monkey pox in pakistan,” Annals of Biomedical Engineering , 5 2023. [Online]. Available: https://link.springer.com/10.1007/s10439-023-03231-z

2023

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B. D. Lund and T. Wang, “Chatting about chatgpt: How may ai and gpt impact academia and libraries?” Library Hi Tech News. , 2023. [Online]. Available: https://ssrn.com/abstract=4333415

2023

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E. Kasneci, K. Sessler, S. K. Uchemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. G. Unnemann, S. Krusche, G. Kutyniok, T. Michaeli, C. Nerdel, J. U. Pfeffer, O. Poquet, M. Sailer, A. Schmidt, T. Seidel, M. Stadler, J. Weller, J. Kuhn, and G. Kasneci, “Chatgpt for good? on opportunities and challenges of large language models for education,” Learning and Individual Differences , vol. 103, 2023

2023

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