{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T14:29:32Z","timestamp":1754144972666,"version":"3.41.2"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,1,10]]},"DOI":"10.1145\/3726101.3726109","type":"proceedings-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T07:32:17Z","timestamp":1752651137000},"page":"40-51","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Reading Comprehension: A Comparative Study of Models for Generating Questions in Children's Storybooks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9069-5470","authenticated-orcid":false,"given":"Thunpitcha","family":"Sattabun","sequence":"first","affiliation":[{"name":"Graduate School of Applied Statistics, National Institute of Development Administration, Bangkok, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6530-5302","authenticated-orcid":false,"given":"Thitirat","family":"Siriborvornratanakul","sequence":"additional","affiliation":[{"name":"Graduate School of Applied Statistics, National Institute of Development Administration, Bangkok, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"e_1_3_3_1_1_2","unstructured":"T .Brown B. Mann N. Ryder M. Subbiah J.D. Kaplan P. Dhariwal A. Neelakantan P. Shyam G. Sastry A. Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 1877\u20131901."},{"key":"e_1_3_3_1_2_2","unstructured":"A.Q. Jiang A. Sablayrolles A. Mensch C. Bamford D.S. Chaplot D.d.l. Casas F. Bressand G. Lengyel G. Lample L. Saulnier et al. 2023. Mistral 7b. arXiv preprint arXiv:2310.06825."},{"key":"e_1_3_3_1_3_2","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.","author":"Touvron H.","year":"2023","unstructured":"H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.A. Lachaux, T. Lacroix, B. Rozi\u00e8re, N. Goyal, E. Hambro, F. Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971."},{"key":"e_1_3_3_1_4_2","unstructured":"G. Team R. Anil S. Borgeaud Y. Wu J.B. Alayrac J. Yu R. Soricut J. Schalkwyk A.M. Dai A. Hauth et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805."},{"volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).","author":"Zeng H.","key":"e_1_3_3_1_5_2","unstructured":"H. Zeng, B. Wei, J. Liu, and W. Fu. 2023. Synthesize, prompt and transfer: Zero-shot conversational question generation with pre-trained language model. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3207569"},{"volume-title":"Proceedings of the 28th International Conference on Computational Linguistics.","author":"Goodwin T.","key":"e_1_3_3_1_7_2","unstructured":"T. Goodwin, M. Savery, and D. Demner-Fushman. 2020. Flight of the PEGASUS? comparing transformers on few-shot and zero-shot multi-document abstractive summarization. In Proceedings of the 28th International Conference on Computational Linguistics."},{"volume-title":"Conference on Empirical Methods in Natural Language Processing. https:\/\/api.semanticscholar.org\/CorpusID:238260199","author":"Schick T.","key":"e_1_3_3_1_8_2","unstructured":"T. Schick and H. Sch\u00fctze. 2020. Few-shot text generation with natural language instructions. In Conference on Empirical Methods in Natural Language Processing. https:\/\/api.semanticscholar.org\/CorpusID:238260199"},{"key":"e_1_3_3_1_9_2","volume-title":"Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA","author":"Gonzalez H.","year":"2023","unstructured":"H. Gonzalez, L. Dugan, E. Miltsakaki, Z. Cui, J. Ren, B. Li, S. Upadhyay, E. Ginsberg, and C. Callison-Burch. 2023. Enhancing human summaries for question-answer generation in education. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)."},{"key":"e_1_3_3_1_10_2","unstructured":"Y. Labrak M. Rouvier and R. Dufour. 2023. A zero-shot and few-shot study of instruction-finetuned large language models applied to clinical and biomedical tasks. arXiv preprint arXiv:2307.12114."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455856"},{"key":"e_1_3_3_1_12_2","unstructured":"H. Touvron L. Martin K. Stone P. Albert A. Almahairi Y. Babaei N. Bashlykov S. Batra P. Bhargava S. Bhosale et al. 2023. Llama 2: Open foundation and fine-tuned chat models. https:\/\/arxiv.org\/abs\/2307.09288"},{"key":"e_1_3_3_1_13_2","first-page":"460","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 1","author":"Xu Y.","unstructured":"Y. Xu, D. Wang, M. Yu, D. Ritchie, B. Yao, T. Wu, Z. Zhang, T.J. Li, N. Bradford, B. Sun, T.B. Hoang, Y. Sang, Y. Hou, X. Ma, D. Yang, N. Peng, Z. Yu, and M. Warschauer. 2022. Fantastic questions and where to find them: FairytaleQA \u2013 an authentic dataset for narrative comprehension. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 1, pp. 447\u2013460."},{"key":"e_1_3_3_1_14_2","volume-title":"Proceedings of the Web Conference","author":"Lelkes A.D.","year":"2021","unstructured":"A.D. Lelkes, V.Q. Tran, and C. Yu. 2021. Quiz-style question generation for news stories. In Proceedings of the Web Conference 2021, 2501\u20132511."},{"key":"e_1_3_3_1_15_2","unstructured":"C. Zheng and M. Huang. 2021. Exploring prompt-based few-shot learning for grounded dialog generation. arXiv preprint arXiv:2109.06513."},{"key":"e_1_3_3_1_16_2","first-page":"2131","article-title":"Question generation for reading comprehension assessment by modeling how and what to ask","volume":"2022","author":"Ghanem B.","year":"2022","unstructured":"B. Ghanem, L. Lutz Coleman, J. Rivard Dexter, S. Ohe, and A. Fyshe. 2022. Question generation for reading comprehension assessment by modeling how and what to ask. In Findings of the Association for Computational Linguistics: ACL 2022, 2131\u20132146.","journal-title":"Findings of the Association for Computational Linguistics: ACL"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"X. Yuan T. Wang Y.H. Wang E. Fine R. Abdelghani H. Sauz\u00e9on and P.Y. Oudeyer. 2023. Selecting better samples from pre-trained LLMs: A case study on question generation. In Rogers A. Boyd-Graber J. Okazaki N. (eds.) 12952\u201312965. Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.findings-acl.820"},{"issue":"10","key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","first-page":"567","DOI":"10.3390\/info14100567","article-title":"Automated assessment of comprehension strategies from self-explanations using llms","volume":"14","author":"Nicula B.","year":"2023","unstructured":"B. Nicula, M. Dascalu, T. Arner, R. Balyan, and D.S. McNamara. 2023. Automated assessment of comprehension strategies from self-explanations using llms. Information, 14(10), 567.","journal-title":"Information"},{"key":"e_1_3_3_1_19_2","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics","volume":"1","author":"Devlin J.","year":"2019","unstructured":"J. Devlin, M.W. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 1, 4171\u20134186.","journal-title":"Human Language Technologies (NAACL)"},{"volume-title":"Proceedings of the Eighth Conference on Machine Translation, 468\u2013481","author":"Zhang X.","key":"e_1_3_3_1_20_2","unstructured":"X. Zhang, N. Rajabi, K. Duh, K., and P. Koehn. 2023. Machine translation with large language models: Prompting, few-shot learning, and fine-tuning with qlora. In Proceedings of the Eighth Conference on Machine Translation, 468\u2013481."},{"volume-title":"International Conference on Learning Representations (ICLR).","author":"Hu E.J.","key":"e_1_3_3_1_21_2","unstructured":"E.J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen. 2022. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR)."}],"event":{"name":"APIT 2025: 2025 7th Asia Pacific Information Technology Conference","acronym":"APIT 2025","location":"Hong Kong China"},"container-title":["Proceedings of the 2025 7th Asia Pacific Information Technology Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3726101.3726109","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T07:32:51Z","timestamp":1752651171000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3726101.3726109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":21,"alternative-id":["10.1145\/3726101.3726109","10.1145\/3726101"],"URL":"https:\/\/doi.org\/10.1145\/3726101.3726109","relation":{},"subject":[],"published":{"date-parts":[[2025,1,10]]},"assertion":[{"value":"2025-07-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}