{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:06:44Z","timestamp":1758931604002,"version":"3.44.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Planning Youth Project of Philosophy and Social Sciences of Anhui","award":["AHSKQ2021D47"],"award-info":[{"award-number":["AHSKQ2021D47"]}]},{"name":"Scientific Research Planning Project in Anhui","award":["2022AH051311"],"award-info":[{"award-number":["2022AH051311"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01290-8","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T10:09:55Z","timestamp":1758881395000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CLSeq and Nscp: novel methods for reducing hallucinations in text summarization for pre-trained models and LLMs"],"prefix":"10.1186","volume":"12","author":[{"given":"Ben","family":"Lu","sequence":"first","affiliation":[]},{"given":"Xianchuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wenkai","family":"Ming","sequence":"additional","affiliation":[]},{"given":"Xianchao","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"1290_CR1","doi-asserted-by":"publisher","unstructured":"See A, Liu PJ, Manning CD. Get to the point: Summarization with pointer-generator networks. In: Barzilay, R., Kan, M.-Y, editors. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics. Canada:Vancouver; 2017. pp. 1073\u20131083. https:\/\/doi.org\/10.18653\/v1\/P17-1099","DOI":"10.18653\/v1\/P17-1099"},{"key":"1290_CR2","doi-asserted-by":"publisher","unstructured":"Inoue N, Trivedi H, Sinha S, Balasubramanian N, Inui K. Summarize-then-answer: Generating concise explanations for multi-hop reading comprehension. In: Moens, M.-F., Huang, X., Specia, L., Yih, S.W.-t, editors Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6064\u20136080. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.490","DOI":"10.18653\/v1\/2021.emnlp-main.490"},{"issue":"1","key":"1290_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s40537-023-00842-0","volume":"11","author":"H Zhang","year":"2024","unstructured":"Zhang H, Shafiq MO. Survey of Transformers and towards ensemble learning using Transformers for natural Language processing. J Big Data. 2024;11(1):25. https:\/\/doi.org\/10.1186\/s40537-023-00842-0.","journal-title":"J Big Data"},{"key":"1290_CR4","doi-asserted-by":"publisher","unstructured":"Dou Z-Y, Liu P, Hayashi H, Jiang Z, Neubig G. GSum: A general framework for guided neural abstractive summarization. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y, editors. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics; Online 2021. pp. 4830\u20134842. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.384","DOI":"10.18653\/v1\/2021.naacl-main.384"},{"key":"1290_CR5","doi-asserted-by":"publisher","unstructured":"Kryscinski W, McCann B, Xiong C, Socher R. Evaluating the factual consistency of abstractive text summarization. In: Webber, B., Cohn, T., He, Y., Liu, Y, editors. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics; Online 2020. pp. 9332\u20139346. https:\/\/doi.org\/10.18653\/v1\/2020. emnlp-main.750.","DOI":"10.18653\/v1\/2020"},{"key":"1290_CR6","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang M, Manzoor MA, Liu F, Georgiev GN, Das RJ, Nakov P. Factuality of large language models: a survey. In: Al-Onaizan, Y., Bansal, M., Chen, Y.-N, editors. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Miami, Florida, USA 2024. pp. 19519\u201319529. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.1088https:\/\/aclanthology.org\/2024.emnlp-main.","DOI":"10.18653\/v1\/2024.emnlp-main.1088"},{"key":"1290_CR7","doi-asserted-by":"publisher","unstructured":"Eid MM, El-Kenawy E-SM, Khodadadi N, Mirjallii S, Khodadadi E, Abotaleb M, Alharbi AH, Abdelhamid AA, Ibrahim A, Amer GM, Kadi A, Khafaga DS. Meta-heuristic optimization of lstm-based deep network for boosting the prediction of Monkeypox cases. Mathematics. 2022;10(20). https:\/\/doi.org\/10.3390\/math10203845.","DOI":"10.3390\/math10203845"},{"key":"1290_CR8","doi-asserted-by":"publisher","unstructured":"Wang Y, Wang M, Manzoor MA, Liu F, Georgiev GN, Das RJ, Nakov P. Factuality of large language models: A survey. In: Al-Onaizan, Y., Bansal, M., Chen, Y.-N, editors. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Miami, Florida USA: Association for Computational Linguistics; 2024. pp. 19519\u201319529.. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main. 1088.","DOI":"10.18653\/v1\/2024.emnlp-main"},{"key":"1290_CR9","doi-asserted-by":"publisher","unstructured":"Li J, Yang Y, Bai Y, Zhou X, Li Y, Sun H, Liu Y, Si X, Ye Y, Wu Y, Lin Y, Xu B, Bowen R, Feng C, Gao Y, Huang H. Fundamental capabilities of large language models and their applications in domain scenarios: A survey. In: Ku, L.-W., Martins, A., Srikumar, V, editors Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Bangkok, Thailand 2024. pp. 11116\u201311141 https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.599","DOI":"10.18653\/v1\/2024.acl-long.599"},{"key":"1290_CR10","doi-asserted-by":"publisher","first-page":"13590","DOI":"10.18653\/v1\/2024.findings-acl.807","volume-title":"Findings of the association for computational linguistics: ACL 2024","author":"D Caffagni","year":"2024","unstructured":"Caffagni D, Cocchi F, Barsellotti L, Moratelli N, Sarto S, Baraldi L, Baraldi L, Cornia M, Cucchiara R. The revolution of multimodal large Language models: A survey. In: Ku L-W, Martins A, Srikumar V, editors. Findings of the association for computational linguistics: ACL 2024. Bangkok, Thailand: Association for Computational Linguistics; 2024. pp. 13590\u2013618. https:\/\/doi.org\/10.18653\/v1\/2024.findings-acl.807."},{"key":"1290_CR11","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.18653\/v1\/2023.findings-acl.112","volume-title":"Findings of the association for computational linguistics: ACL 2023","author":"D Li","year":"2023","unstructured":"Li D, Rawat AS, Zaheer M, Wang X, Lukasik M, Veit A, Yu F, Kumar S. Large Language models with controllable working memory. In: Rogers A, Boyd-Graber J, Okazaki N, editors. Findings of the association for computational linguistics: ACL 2023. Toronto, Canada: Association for Computational Linguistics; 2023. pp. 1774\u201393. https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.112."},{"key":"1290_CR12","doi-asserted-by":"publisher","unstructured":"Dong Q, Li L, Dai D, Zheng C, Ma J, Li R, Xia H, Xu J, Wu Z, Chang B, Sun X, Li L, Sui Z. A survey on in-context learning. In: Al-Onaizan, Y., Bansal, M., Chen, Y.-N, editors Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Miami, Florida, USA: Association for Computational Linguistics; 2024. pp. 1107\u20131128. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.64","DOI":"10.18653\/v1\/2024.emnlp-main.64"},{"key":"1290_CR13","doi-asserted-by":"publisher","unstructured":"Wang B, Min S, Deng X, Shen J, Wu Y, Zettlemoyer L, Sun H. Towards understanding chain-of-thought prompting: An empirical study of what matters. In: Rogers, A., Boyd-Graber, J., Okazaki, N, editors. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Toronto, Canada: Association for Computational Linguistics; 2023. pp. 2717\u20132739. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.153","DOI":"10.18653\/v1\/2023.acl-long.153"},{"key":"1290_CR14","unstructured":"Sun S, Li W. Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization (2021). https:\/\/arxiv.org\/abs\/2108.11846"},{"key":"1290_CR15","doi-asserted-by":"publisher","unstructured":"Liu Y, Liu P. SimCLS: A simple framework for contrastive learning of abstractive summarization. In: Zong, C., Xia, F., Li, W., Navigli, R, editors. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, Online 2021. pp. 1065\u20131072. https:\/\/doi.org\/10.18653\/v1\/2021.acl-short.135","DOI":"10.18653\/v1\/2021.acl-short.135"},{"key":"1290_CR16","doi-asserted-by":"publisher","unstructured":"Liu Y, Liu P, Radev D, Neubig G. BRIO: Bringing order to abstractive summarization. In: Mure-san, S., Nakov, P., Villavicencio, A, editors. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Dublin, Ireland: Association for Computational Linguistics; 2022. pp. 2890\u20132903. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.207","DOI":"10.18653\/v1\/2022.acl-long.207"},{"key":"1290_CR17","unstructured":"Grill J-B, Strub F, Altch\u00e9 F, Tallec C, Richemond PH, Buchatskaya E, Doersch C, Pires BA, Guo ZD, Azar MG, Piot B, Kavukcuoglu K, Munos R, Valko M. Bootstrap your own latent: a new approach to self-supervised Learning (2020). https:\/\/arxiv.org\/abs\/ 2006.07733."},{"key":"1290_CR18","unstructured":"Xie Q, Huang J, Saha T, Ananiadou S. GRETEL: Graph contrastive topic enhanced language model for long document extractive summarization. In: Calzolari, N., Huang, C.-R., Kim, H., Pustejovsky, J., Wanner, L., Choi, K.-S., Ryu, P.-M., Chen, H.-H., Donatelli, L., Ji, H., Kurohashi, S., Paggio, P., Xue, N., Kim, S., Hahm, Y., He, Z., Lee, T.K., Santus, E., Bond, F., Na, S.-H, editors. Proceedings of the 29th International Conference on Computational Linguistics. Gyeongju, Republic of Korea: International Committee on Computational Linguistics; 2022. pp. 6259\u20136269. https:\/\/aclanthology.org\/2022.coling-1.546\/."},{"issue":"2","key":"1290_CR19","doi-asserted-by":"publisher","first-page":"e0278491","DOI":"10.1371\/journal.pone.0278491","volume":"18","author":"ESM El-kenawy","year":"2023","unstructured":"El-kenawy ESM, Mirjalili S, Khodadadi N, Abdelhamid AA, Eid MM, et al. Feature selection in wind speed forecasting systems based on meta-heuristic optimization. PLoS ONE. 2023;18(2):e0278491. https:\/\/doi.org\/10.1371\/journal.pone.0278491.","journal-title":"PLoS ONE"},{"key":"1290_CR20","doi-asserted-by":"publisher","unstructured":"Grusky M, Naaman M, Artzi Y. Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies. In: Walker, M., Ji, H., Stent, A, editors. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans, Louisiana: Association for Computational Linguistics; 2018. pp. 708\u2013719. https:\/\/doi.org\/10.18653\/v1\/N18-1065. https:\/\/aclanthology.org\/N18-1065\/.","DOI":"10.18653\/v1\/N18-1065"},{"key":"1290_CR21","doi-asserted-by":"publisher","unstructured":"Alami Merrouni Z, Frikh B, Ouhbi B. Exabsum: a new text summarization approach for generating extractive and abstractive summaries. J Big Data. 2023;10(1). https:\/\/doi.org\/10.1186\/s40537-023-00836-y. ),163.","DOI":"10.1186\/s40537-023-00836-y"},{"key":"1290_CR22","doi-asserted-by":"publisher","unstructured":"Zhu C, Hinthorn W, Xu R, Zeng Q, Zeng M, Huang X, Jiang M. Enhancing factual consistency of abstractive summarization. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tur, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y, editors. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online 2021. pp. 718\u2013733.. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.58","DOI":"10.18653\/v1\/2021.naacl-main.58"},{"key":"1290_CR23","doi-asserted-by":"publisher","unstructured":"Cao M, Dong Y, Wu J, Cheung JCK. Factual error correction for abstractive summarization models. In: Webber, B., Cohn, T., He, Y., Liu, Y, editors. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online 2020. pp. 6251\u20136258. https:\/\/doi.org\/10.18653\/v1\/2020. emnlp-main.506.","DOI":"10.18653\/v1\/2020"},{"key":"1290_CR24","doi-asserted-by":"publisher","unstructured":"Thorne J, Vlachos A. Evidence-based factual error correction. In: Zong, C., Xia, F., Li, W., Navigli, R, editors. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online 2021. pp. 3298\u20133309. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.256","DOI":"10.18653\/v1\/2021.acl-long.256"},{"key":"1290_CR25","unstructured":"Xie Q, Zhou J, Peng Y, Wang F. FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization (2023). https:\/\/arxiv.org\/abs\/2303.08335"},{"key":"1290_CR26","unstructured":"Tonmoy SMTI, Zaman SMM, Jain V, Rani A, Rawte V, Chadha A, Das A. A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models 2024. https:\/\/arxiv.org\/abs\/2401.01313"},{"key":"1290_CR27","unstructured":"Zhang Y, Li Y, Cui L, Cai D, Liu L, Fu T, Huang X, Zhao E, Zhang Y, Chen Y, Wang L, Luu AT, Bi W, Shi F, Shi S. Siren\u2019s Song in the AI Ocean: A Survey on Hallucination in Large Language Models 2023. https:\/\/arxiv.org\/abs\/2309.01219."},{"key":"1290_CR28","doi-asserted-by":"publisher","first-page":"3270","DOI":"10.18653\/v1\/2023.findings-emnlp.214","volume-title":"Findings of the association for computational linguistics: EMNLP 2023","author":"H Zhang","year":"2023","unstructured":"Zhang H, Liu X, Zhang J. Extractive summarization via ChatGPT for faithful summary generation. In: Bouamor H, Pino J, Bali K, editors. Findings of the association for computational linguistics: EMNLP 2023. Singapore: Association for Computational Linguistics; 2023. pp. 3270\u20138. https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.214."},{"key":"1290_CR29","unstructured":"Li T, Li Z, Zhang Y. Improving faithfulness of large language models in summarization via sliding generation and self-consistency. In: Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., Xue, N, editors. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL, Torino, Italia 2024. pp. 8804\u20138817. https:\/\/aclanthology.org\/2024.lrec-main.771\/."},{"key":"1290_CR30","doi-asserted-by":"publisher","first-page":"10644","DOI":"10.18653\/v1\/2023.findings-emnlp.714","volume-title":"Findings of the association for computational linguistics: EMNLP 2023","author":"H Zhang","year":"2023","unstructured":"Zhang H, Liu X, Zhang J. SummIt: iterative text summarization via ChatGPT. In: Bouamor H, Pino J, Bali K, editors. Findings of the association for computational linguistics: EMNLP 2023. Singapore: Association for Computational Linguistics; 2023. pp. 10644\u201357. https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.714."},{"key":"1290_CR31","doi-asserted-by":"publisher","unstructured":"Nan F, Nallapati R, Wang Z, Santos C, Zhu H, Zhang D, McKeown K, Xiang B. Entity-level factual consistency of abstractive text summarization. In: Merlo, P., Tiedemann, J., Tsarfaty, R, editors. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics, Online 2021. pp. 2727\u20132733. https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.235","DOI":"10.18653\/v1\/2021.eacl-main.235"},{"key":"1290_CR32","doi-asserted-by":"publisher","first-page":"122147","DOI":"10.1016\/j.eswa.2023.122147","volume":"238","author":"E-SM El-kenawy","year":"2024","unstructured":"El-kenawy E-SM, Khodadadi N, Mirjalili S, Abdelhamid AA, Eid MM, Ibrahim A. Greylag Goose optimization: Nature-inspired optimization algorithm. Expert Syst Appl. 2024;238:122147. https:\/\/doi.org\/10.1016\/j.eswa.2023.122147.","journal-title":"Expert Syst Appl"},{"key":"1290_CR33","doi-asserted-by":"publisher","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020. pp. 9726\u20139735 https:\/\/doi.org\/10.1109\/CVPR42600.2020.00975","DOI":"10.1109\/CVPR42600.2020.00975"},{"issue":"1","key":"1290_CR34","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s40537-024-00963-0","volume":"11","author":"J Yeom","year":"2024","unstructured":"Yeom J, Lee H, Byun H, Kim Y, Byun J, Choi Y, Kim S, Song K. Tc-llama 2: fine-tuning Llm for technology and commercialization applications. J Big Data. 2024;11(1):100. https:\/\/doi.org\/10.1186\/s40537-024-00963-0.","journal-title":"J Big Data"},{"key":"1290_CR35","doi-asserted-by":"publisher","unstructured":"Wu W, Li W, Xiao X, Liu J, Li S, Lyu Y. WeCheck: Strong factual consistency checker via weakly supervised learning. In: Rogers, A., Boyd-Graber, J., Okazaki, N, editors. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Toronto, Canada 2023. pp. 307\u2013321. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long. 18.","DOI":"10.18653\/v1\/2023.acl-long"},{"key":"1290_CR36","doi-asserted-by":"publisher","unstructured":"Scialom T, Dray P-A, Lamprier S, Piwowarski B, Staiano J, Wang A, Gallinari P. QuestEval: Summarization asks for fact-based evaluation. In: Moens, M.-F., Huang, X., Specia, L., Yih, S.W.-t, editors. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic 2021. pp. 6594\u20136604. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.529","DOI":"10.18653\/v1\/2021.emnlp-main.529"},{"key":"1290_CR37","doi-asserted-by":"crossref","unstructured":"Liu Y, Iter D, Xu Y, Wang S, Xu R, Zhu C. G-Eval: NLG evaluation using GPT-4 with better human alignment (2023). https:\/\/arxiv.org\/abs\/2303.16634","DOI":"10.18653\/v1\/2023.emnlp-main.153"},{"key":"1290_CR38","unstructured":"Zhang J, Zhao Y, Saleh M, Liu PJ. PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization 2020. https:\/\/arxiv.org\/abs\/1912.08777"},{"key":"1290_CR39","doi-asserted-by":"crossref","unstructured":"He Z, Yang M, Feng M, Yin J, Wang X, Leng J, Lin Z. Fourier transformer: Fast long range modeling by removing sequence redundancy with FFT operator. In: Rogers, A., Boyd-Graber, J., Okazaki, N, editors. Findings of the Association for Computational Linguistics: ACL 2023. Toronto, Canada: Association for Computational Linguistics; 2023. pp. 8954\u20138966. https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.570https:\/\/aclanthology.org\/2023.findings-acl.570\/.","DOI":"10.18653\/v1\/2023.findings-acl.570"},{"key":"1290_CR40","doi-asserted-by":"crossref","unstructured":"Wang F, Song K, Zhang H, Jin L, Cho S, Yao W, Wang X, Chen M, Yu D. Salience allocation as guidance for abstractive summarization. In: Goldberg, Y., Kozareva, Z., Zhang, Y, editors Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Abu Dhabi, United Arab Emirates: Association for Computational Linguistics; 2022. pp. 6094\u20136106.https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.409https:\/\/aclanthology.org\/2022.emnlp-main.409\/.","DOI":"10.18653\/v1\/2022.emnlp-main.409"},{"key":"1290_CR41","unstructured":"Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J, Hilton J, Kelton F, Miller L, Simens M, Askell A, Welinder P, Christiano P, Leike J, Lowe R. Training language models to follow instructions with human feedback 2022. https:\/\/arxiv.org\/abs\/2203.02155."},{"key":"1290_CR42","unstructured":"OpenAI, Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, Almeida D, Altenschmidt J, Altman S, Anadkat S, Avila R. GPT-4 Technical Report 2024. https:\/\/arxiv.org\/abs\/2303.08774"},{"key":"1290_CR43","unstructured":"Team G, Georgiev P, Lei VI, Burnell R, Bai L, Gulati A, Tanzer G, Vincent D, Pan Z. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context 2024. https:\/\/arxiv.org\/abs\/2403.05530"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01290-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01290-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01290-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T10:10:02Z","timestamp":1758881402000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01290-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1290"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01290-8","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]},"assertion":[{"value":"28 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and consent to participate declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"220"}}