{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:29:05Z","timestamp":1775953745977,"version":"3.50.1"},"reference-count":106,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T00:00:00Z","timestamp":1764979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100020716","name":"Wenzhou-Kean University","doi-asserted-by":"publisher","award":["IRSPK2023005"],"award-info":[{"award-number":["IRSPK2023005"]}],"id":[{"id":"10.13039\/100020716","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s44443-025-00334-6","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T12:53:30Z","timestamp":1765025610000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving negative rejection ability in language models: A review of fine-tuned LLMs, RAG, and RAFT"],"prefix":"10.1007","volume":"38","author":[{"given":"Li","family":"Bowen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Zhuoqing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Hengyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sun","family":"Xubin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2109-4793","authenticated-orcid":false,"given":"Baha","family":"Ihnaini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"key":"334_CR1","doi-asserted-by":"publisher","unstructured":"Abbasiantaeb Z, Aliannejadi M (2024) Generate then retrieve: Rethinking conversational response retrieval with llms as answer and query generators. arXiv preprint arXiv:2403.19302. https:\/\/doi.org\/10.48550\/arXiv.2403.19302","DOI":"10.48550\/arXiv.2403.19302"},{"issue":"5","key":"334_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3708982","volume":"57","author":"W Ali","year":"2025","unstructured":"Ali W, Zhou X, Shao J (2025) Privacy-preserved and responsible recommenders: from conventional defense to federated learning and blockchain. ACM Comput Surv 57(5):1\u201335. https:\/\/doi.org\/10.1145\/3708982","journal-title":"ACM Comput Surv"},{"key":"334_CR3","doi-asserted-by":"publisher","unstructured":"Asai A, Kasai J, Clark JH, Lee K, Choi E, Hajishirzi H (2021) XOR QA: Cross-lingual open-retrieval question answering. Proceedings of NAACL-HLT. 489\u2013503. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.46","DOI":"10.18653\/v1\/2021.naacl-main.46"},{"key":"334_CR4","unstructured":"Asai A, Wu Z, Wang Y, Sil A, Hajishirzi H (2024) Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024). Retrieved from https:\/\/openreview.net\/forum?id=hSyW5go0v8"},{"key":"334_CR5","doi-asserted-by":"publisher","unstructured":"Badampudi D, Wohlin C, Petersen K (2015) Experiences from using snowballing and database searches in systematic literature studies. In Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering (EASE 2015), Article No. 17. Association for Computing Machinery. https:\/\/doi.org\/10.1145\/2745802.2745818","DOI":"10.1145\/2745802.2745818"},{"key":"334_CR6","doi-asserted-by":"publisher","unstructured":"Bai Y, Kadavath S, Kundu S, Askell A, Kernion J, Jones A, Chen A, Goldie A, Mirhoseini A, McKinnon C, Chen C, Olsson C, Olah C, Hernandez D, Drain D, Ganguli D, Li D, Tran-Johnson E, Perez E, \u2026 Kaplan J (2022) Constitutional AI: Harmlessness from AI feedback (arXiv Preprint No. 2212.08073). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2212.08073","DOI":"10.48550\/arXiv.2212.08073"},{"key":"334_CR7","unstructured":"Bengio Y, Mindermann S, Privitera D (2025) International AI Safety Report 2025: Advanced AI Risks and Mitigation, AI Safety Institute. United Kingdom. Retrieved from https:\/\/coilink.org\/20.500.12592\/30e27x2 on 04 Sep 2025. COI: 20.500.12592\/30e27x2."},{"key":"334_CR8","doi-asserted-by":"publisher","unstructured":"Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, von Arx S, Bernstein MS, Bohg J, Bosselut A, Brunskill E, Brynjolfsson E, Buch S, Card D, Castellon R, Chatterji N, Chen A, Creel K, Davis JQ, Demszky D, \u2026 Liang P (2021) On the opportunities and risks of foundation models (arXiv Preprint No. 2108.07258). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2108.07258","DOI":"10.48550\/arXiv.2108.07258"},{"key":"334_CR9","unstructured":"Borgeaud S, Mensch A, Hoffmann J, Cai T, Rutherford E, Millican K, van den Driessche GB, Lespiau J, Damoc B, Clark A, de Las Casas D, Guy A, Menick J, Ring R, Hennigan T, Huang S, Maggiore L, Jones C, Cassirer A, \u2026 Sifre L (2022) Improving language models by retrieving from trillions of tokens. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesv\u00e1ri, G. Niu, & S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning (PMLR Vol. 162, pp. 2206\u20132240). Proceedings of Machine Learning Research. https:\/\/proceedings.mlr.press\/v162\/borgeaud22a.html"},{"key":"334_CR10","doi-asserted-by":"publisher","unstructured":"Boulesnane A, Souilah A (2024) An evolutionary large language model for hallucination mitigation. In Proceedings of the 1st International Conference on Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech 2024) (pp. 1\u20138). IEEE. https:\/\/doi.org\/10.1109\/ECTE-Tech62477.2024.10851107","DOI":"10.1109\/ECTE-Tech62477.2024.10851107"},{"key":"334_CR11","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S, Herbert-Voss A, Krueger G, Henighan T, Child R, Ramesh A, Ziegler DM, Wu J, Winter C, \u2026 Amodei D (2020) Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems, 33 (pp. 1877\u20131901). Curran Associates. https:\/\/arxiv.org\/abs\/2005.14165"},{"key":"334_CR12","doi-asserted-by":"publisher","unstructured":"Cao L (2024) Learn to refuse: Making large language models more controllable and reliable through knowledge scope limitation and refusal mechanism. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 3628\u20133646). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.212","DOI":"10.18653\/v1\/2024.emnlp-main.212"},{"key":"334_CR13","doi-asserted-by":"publisher","unstructured":"Chang Y, Wang X, Wang J, Wu Y, Yang L, Zhu K, Chen H, Yi X, Wang C, Wang Y, Ye W, Zhang Y, Chang Y, Yu PS, Yang Q, Xie X, \u2026 Yu PS (2024) A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3), Article 39, 1\u201345. https:\/\/doi.org\/10.1145\/3641289","DOI":"10.1145\/3641289"},{"key":"334_CR14","first-page":"107003","volume":"37","author":"S Chaudhary","year":"2025","unstructured":"Chaudhary S, Dinesha U, Kalathil D, Shakkottai S (2025) Risk-averse fine-tuning of large language models. Adv Neural Inf Process Syst 37:107003\u2013107038","journal-title":"Adv Neural Inf Process Syst"},{"key":"334_CR15","doi-asserted-by":"publisher","first-page":"29728","DOI":"10.1609\/aaai.v38i16.29728","volume":"38","author":"J Chen","year":"2024","unstructured":"Chen J, Lin H, Han X, Sun L, AAAI Press (2024) Benchmarking large language models in retrieval-augmented generation. Proceedings of the AAAI Conference on Artificial Intelligence 38:29728. https:\/\/doi.org\/10.1609\/aaai.v38i16.29728","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"334_CR16","doi-asserted-by":"publisher","unstructured":"Chen J, Shi W, Fu Z, Cheng S, Li L, Xiao Y (2023) Say what you mean! Large language models speak too positively about negative commonsense knowledge. In: Proceedings of the 61st annual meeting of the association for computational linguistics (Volume 1: Long Papers). Association for Computational Linguistics, pp 9890\u20139908. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.550","DOI":"10.18653\/v1\/2023.acl-long.550"},{"key":"334_CR17","doi-asserted-by":"publisher","unstructured":"Choi C, Lee Y, Chen A, Zhou A, Raghunathan A, Finn C (2024) AutoFT: Learning an objective for robust fine-tuning (arXiv Preprint No. 2401.10220). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2401.10220","DOI":"10.48550\/arXiv.2401.10220"},{"key":"334_CR18","first-page":"8765","volume-title":"Advances in Neural Information Processing Systems, 33","author":"C-Y Chuang","year":"2020","unstructured":"Chuang C-Y, Robinson J, Lin Y-C, Torralba A, Jegelka S (2020) Debiased contrastive learning. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in Neural Information Processing Systems, 33. Curran Associates, pp 8765\u20138775"},{"key":"334_CR19","doi-asserted-by":"publisher","unstructured":"Clark JH, Choi E, Collins M, Garrette D, \u2026 Lee K (2020) TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. Transactions of the ACL, 8, 454\u2013470. https:\/\/doi.org\/10.1162\/tacl_a_00317","DOI":"10.1162\/tacl_a_00317"},{"key":"334_CR20","doi-asserted-by":"publisher","unstructured":"Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm\u00e1n F, \u2026 Stoyanov V (2020) Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 8440\u20138451). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.747","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"334_CR21","unstructured":"Cui J, Chiang W-L, Stoica I, Hsieh C-J (2025) OR-Bench: an over-refusal benchmark for large language models. In: Proceedings of ICML 2025. https:\/\/arxiv.org\/abs\/2405.20947"},{"key":"334_CR22","doi-asserted-by":"crossref","unstructured":"Dettmers T, Pagnoni A, Holtzman A, Zettlemoyer L (2023) QLoRA: efficient finetuning of quantized LLMs. In: Advances in neural information processing systems, vol 36. Curran Associates. https:\/\/arxiv.org\/abs\/2305.14314","DOI":"10.52202\/075280-0441"},{"key":"334_CR23","doi-asserted-by":"publisher","unstructured":"Dhuliawala S, Komeili M, Xu J, Raileanu R, Li X, \u00c7elikyilmaz A, Weston J (2024) Chain-of-verification reduces hallucination in large language models. In Findings of the Association for Computational Linguistics: ACL 2024 (pp. 3563\u20133578). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2024.findings-acl.212","DOI":"10.18653\/v1\/2024.findings-acl.212"},{"key":"334_CR24","doi-asserted-by":"publisher","unstructured":"Edge D, Lee Y, Chen A, Zhou A, Raghunathan A, Finn C (2024) From local to global: A graph RAG approach to query-focused summarization (arXiv Preprint No. 2404.16130). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2404.16130","DOI":"10.48550\/arXiv.2404.16130"},{"key":"334_CR25","doi-asserted-by":"publisher","unstructured":"Fang F, Bai Y, Ni S, Yang M, Chen X, Xu R (2024a) En- hancing noise robustness of retrieval-augmented language models with adaptive adversarial training. In Proc. 62nd annu. meeting of the association for computational linguistics (acl 2024). Bangkok, Thailand. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.540","DOI":"10.18653\/v1\/2024.acl-long.540"},{"key":"334_CR26","doi-asserted-by":"publisher","unstructured":"Fang F, Bai Y, Ni S, Yang M, Chen X, Xu R (2024b) Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024, Volume 1: Long Papers) (pp. 10028\u201310039). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.540","DOI":"10.18653\/v1\/2024.acl-long.540"},{"issue":"8017","key":"334_CR27","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s41586-024-07421-0","volume":"630","author":"S Farquhar","year":"2024","unstructured":"Farquhar S, Kossen J, Kuhn L, Gal Y (2024) Detecting hallucinations in large language models using semantic entropy. Nature 630(8017):625\u2013630. https:\/\/doi.org\/10.1038\/s41586-024-07421-0","journal-title":"Nature"},{"key":"334_CR28","doi-asserted-by":"publisher","unstructured":"Feldman P, Foulds JR, Pan S (2024) RAGged edges: The double-edged sword of retrieval-augmented chatbots (arXiv Preprint No. 2403.01193). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2403.01193","DOI":"10.48550\/arXiv.2403.01193"},{"key":"334_CR29","doi-asserted-by":"publisher","unstructured":"Gao Y, Xiong Y, Gao X, Jia K, Pan J, Bi Y, Dai Y, Sun J, Wang M, Wang H (2023) Retrieval-augmented generation for large language models: A survey (arXiv Preprint No. 2312.10997). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2312.10997","DOI":"10.48550\/arXiv.2312.10997"},{"key":"334_CR30","doi-asserted-by":"publisher","unstructured":"Garc\u00eda-Ferrero I, Altuna B, \u00c1lvez J, Gonzalez-Dios I, Rigau G (2023) This is not a dataset: A large negation benchmark to challenge large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 8596\u20138615). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.531","DOI":"10.18653\/v1\/2023.emnlp-main.531"},{"key":"334_CR31","doi-asserted-by":"publisher","unstructured":"Gekhman Z, Yona G, Aharoni R, Eyal M, Feder A, Reichart R, Herzig J (2024) Does fine-tuning large language models on new knowledge encourage hallucinations? (arXiv Preprint No. 2405.05904). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2405.05904","DOI":"10.48550\/arXiv.2405.05904"},{"key":"334_CR32","unstructured":"Greyling C (2024) RAFT: Combining RAG with fine-tuning. SuperAnnotate Blog. https:\/\/www.superannotate.com\/blog\/raft-retrieval-augmented-fine-tuning"},{"key":"334_CR33","doi-asserted-by":"publisher","unstructured":"Gupta S et al (2025) Selective self-to-supervised fine-tuning for generalization in large language models (arXiv Preprint No. 2502.08130). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2502.08130","DOI":"10.48550\/arXiv.2502.08130"},{"key":"334_CR34","doi-asserted-by":"publisher","unstructured":"Han Z, Gao C, Liu J, Zhang J, Zhang SQ (2024) Parameter-efficient fine-tuning for large models: A comprehensive survey (arXiv Preprint No. 2403.14608). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2403.14608","DOI":"10.48550\/arXiv.2403.14608"},{"key":"334_CR35","unstructured":"Hu, EJ, Shen Y, Wallis P, Allen-Zhu Z, Li Y, Wang S, Wang L, Chen W (2022) LoRA: Low-rank adaptation of large language models. In Proceedings of the 10th International Conference on Learning Representations. OpenReview. https:\/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"334_CR36","doi-asserted-by":"publisher","unstructured":"Hu Z, Wang L, Lan Y, Xu W, Lim E-P, Bing L, Xu X, Poria S, Lee R (2023) LLM-Adapters: An adapter family for parameter-efficient fine-tuning of large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 5254\u20135276). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.319","DOI":"10.18653\/v1\/2023.emnlp-main.319"},{"key":"334_CR37","doi-asserted-by":"publisher","unstructured":"Huang T, Hu S, Ilhan F, Tekin SF, Liu L (2024) Booster: Tackling harmful fine-tuning for large language models via attenuating harmful perturbation (arXiv Preprint No. 2409.01586). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2409.01586","DOI":"10.48550\/arXiv.2409.01586"},{"key":"334_CR38","doi-asserted-by":"publisher","unstructured":"Huber-Flifle, N, Zhang J, Gronvall P, Wei F, Spinelli P (2024) Experimental study of in-context learning for text classification and its application to legal document review in construction delay disputes. In Proceedings of the 2024 IEEE International Conference on Big Data (BigData) (pp. 2119\u20132125). IEEE. https:\/\/doi.org\/10.1109\/BigData62323.2024.10826061","DOI":"10.1109\/BigData62323.2024.10826061"},{"key":"334_CR39","unstructured":"Izacard G, Grave E, Joulin A (2021) Unsupervised dense information retrieval with contrastive learning (Contriever). arXiv:2112.09118. https:\/\/arxiv.org\/abs\/2112.09118"},{"key":"334_CR40","unstructured":"Izacard G, Lewis P, Lomeli M, Hosseini L, Petroni F, Schick T, \u2026 Grave E (2023) Atlas: Few-shot learning with retrieval-augmented language models. Journal of Machine Learning Research, 24(251), 11912\u201311954."},{"key":"334_CR41","unstructured":"Jacovi A, Wang A, Alberti C, Tao C, Lipovetz J, Olszewska K, ... Das D (2025). The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input. arXiv preprint arXiv:2501.03200."},{"key":"334_CR42","doi-asserted-by":"publisher","unstructured":"Jayawardena L, Yapa P (2024) Improving quality and domain-relevancy of paraphrase generation with graph-based retrieval-augmented generation. In Proceedings of the 10th International Conference on Computing and Artificial Intelligence (ICCAI \u201924) (pp. 196\u2013208). Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3669754.3669784","DOI":"10.1145\/3669754.3669784"},{"key":"334_CR43","unstructured":"Joren H, Zhang J, Ferng C-S, Juan D-C, Taly A, Rashtchian C (2024) Sufficient Context: A New Lens on Retrieval Augmented Generation Systems. The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Jjr2Odj8DJ"},{"key":"334_CR44","doi-asserted-by":"publisher","unstructured":"Karpukhin V, Oguz B, Min S, Lewis P, Wu L, Edunov S, Chen D, Yih W-T (2020) Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 6769\u20136781). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.550","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"334_CR45","unstructured":"Kitchenham B (2004) Procedures for performing systematic reviews (Technical Report TR\/SE-0401). Keele University. https:\/\/www.inf.ufsc.br\/%7ealdo.vw\/kitchenham.pdf"},{"key":"334_CR46","doi-asserted-by":"publisher","unstructured":"Kwiatkowski T, Palomaki J, Redfield O, \u2026 Das D (2019) Natural Questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7, 452\u2013466. https:\/\/doi.org\/10.1162\/tacl_a_00276","DOI":"10.1162\/tacl_a_00276"},{"key":"334_CR47","doi-asserted-by":"crossref","unstructured":"Lee S, Kim S, Yousefpour A, Seo M, Yoo KM, Yu Y (2024) Aligning large language models by on-policy self-judgment. arXiv:2402.11253. https:\/\/arxiv.org\/abs\/2402.11253","DOI":"10.18653\/v1\/2024.acl-long.617"},{"key":"334_CR48","unstructured":"Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N et al (2020) Retrieval-augmented generation for knowledge-intensive nlp tasks.\u00a0Adv Neural Inf Process Syst\u00a033:9459\u20139474"},{"key":"334_CR49","doi-asserted-by":"publisher","unstructured":"Li X, Jia X, Jing X-Y (2020) Negative-aware training: be aware of negative samples. In: Proceedings of the 24th European conference on artificial intelligence. IOS Press, pp 1269\u20131275. https:\/\/doi.org\/10.3233\/FAIA200228","DOI":"10.3233\/FAIA200228"},{"key":"334_CR50","doi-asserted-by":"publisher","unstructured":"Li H, Guo D, Li D et al (2024a) PrivLM-Bench: a multi-level privacy evaluation benchmark for language models. In: Proceedings of the 62nd annual meeting of the association for computational linguistics (Volume 1: Long Papers), Bangkok, Thailand. Association for Computational Linguistics, pp 54\u201373. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.4","DOI":"10.18653\/v1\/2024.acl-long.4"},{"key":"334_CR51","doi-asserted-by":"publisher","unstructured":"Li J, Huang M, Zeng Z, Yu C (2024b) Enhancements in data querying: Applying MMR-integrated in-context learning to LLM-based text-to-SQL. In Proceedings of the 2024 China Automation Congress (CAC) (pp. 4830\u20134833). IEEE. https:\/\/doi.org\/10.1109\/CAC63892.2024.10865575","DOI":"10.1109\/CAC63892.2024.10865575"},{"key":"334_CR52","doi-asserted-by":"crossref","unstructured":"Li H, Guo D, Li D, Fan W, Hu Q, Chan C, Yao D, Yao Y, Song Y (2024c) PrivLM-Bench: a multi-level privacy evaluation benchmark for language models. In: Proceedings of the 62nd annual meeting of the association for computational linguistics (Volume 1: Long Papers), Bangkok, Thailand. Association for Computational Linguistics, pp 54\u201373. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.4","DOI":"10.18653\/v1\/2024.acl-long.4"},{"key":"334_CR53","doi-asserted-by":"publisher","unstructured":"Lin XV, Chen X, Chen M, Shi W, Lomeli M, James R, \u2026 Yih W-T (2023) RA-DIT: Retrieval-augmented dual instruction tuning (arXiv Preprint No. 2310.01352). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2310.01352","DOI":"10.48550\/arXiv.2310.01352"},{"key":"334_CR54","doi-asserted-by":"crossref","unstructured":"Liu W, Zhou P, Zhao Z, \u2026 Xu X (2020) K-BERT: Enabling language representation with knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 34(3), 2901\u20132908..","DOI":"10.1609\/aaai.v34i03.5681"},{"key":"334_CR55","doi-asserted-by":"publisher","unstructured":"Liu S-Y, Wang C-Y, Yin H, Molchanov P, Wang Y-CF, Cheng K-T, Chen M-H (2024) DoRA: Weight-decomposed low-rank adaptation (arXiv Preprint No. 2402.09353). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2402.09353","DOI":"10.48550\/arXiv.2402.09353"},{"key":"334_CR56","doi-asserted-by":"publisher","unstructured":"Liu J, Qiu Z, Li Z, Dai Q, Zhu J, Hu M, Yang M, King I (2025a) A survey of personalized large language models: Progress and future directions (arXiv Preprint No. 2502.11528). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2502.11528","DOI":"10.48550\/arXiv.2502.11528"},{"key":"334_CR57","unstructured":"Liu S, Ye H, Zou J (2025b) Reducing Hallucinations in Large Vision-Language Models via Latent Space Steering. The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=LBl7Hez0fF"},{"key":"334_CR58","doi-asserted-by":"publisher","unstructured":"De Luis Balaguer MA, Benara V, de Freitas Cunha RL, et al (2024) RAG vs fine-tuning: Pipelines, trade-offs, and a case study on agriculture (arXiv Preprint No. 2401.08406). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2401.08406","DOI":"10.48550\/arXiv.2401.08406"},{"key":"334_CR59","doi-asserted-by":"publisher","unstructured":"Luo Q, Ma X, Wei X (2024) Enhancing code generation for dataflow programming: Fine-tuning large language models with the DFCPP dataset. In Proceedings of the 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) (pp. 2246\u20132247). IEEE. https:\/\/doi.org\/10.1109\/ISPA63168.2024.00314","DOI":"10.1109\/ISPA63168.2024.00314"},{"issue":"2","key":"334_CR60","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1111\/jels.12413","volume":"22","author":"V Magesh","year":"2025","unstructured":"Magesh V, Surani F, Dahl M, Suzgun M, Manning CD, Ho DE (2025) Hallucination-free? Assessing the reliability of leading AI legal research tools. J Empir Leg Stud 22(2):216\u2013242. https:\/\/doi.org\/10.1111\/jels.12413","journal-title":"J Empir Leg Stud"},{"key":"334_CR61","doi-asserted-by":"publisher","unstructured":"Mhammad AF, Agarwal R, Columbo T, Vigorito J (2023) Generative & responsible AI \u2013 LLMs use in differential governance. In Proceedings of the 2023 International Conference on Computational Science and Computational Intelligence (pp. 291\u2013295). IEEE. https:\/\/doi.org\/10.1109\/CSCI62032.2023.00051","DOI":"10.1109\/CSCI62032.2023.00051"},{"issue":"2","key":"334_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605943","volume":"56","author":"B Min","year":"2023","unstructured":"Min B, Ross H, Sulem E, Pouran Ben Veyseh A, Nguyen TH, Sainz O, Roth D (2023) Recent advances in natural language processing via large pre-trained language models: a survey. ACM Comput Surv 56(2):1\u201340. https:\/\/doi.org\/10.1145\/3605943","journal-title":"ACM Comput Surv"},{"key":"334_CR63","doi-asserted-by":"publisher","unstructured":"Oliveira VD, Bezerra YF, Weigang L, Brom PC, Celestino VRR (2024) SLIM-RAFT: A novel fine-tuning approach to improve cross-linguistic performance for Mercosur common nomenclature (arXiv Preprint No. 2408.03936). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2408.03936","DOI":"10.48550\/arXiv.2408.03936"},{"key":"334_CR64","unstructured":"Oracle Cloud Infrastructure (2024) Scenario 2: Retrieval-Augmented Generation (RAG) benchmarks in generative AI. Oracle Help Center. https:\/\/docs.oracle.com\/en-us\/iaas\/Content\/generative-ai\/scenario-2.htm"},{"key":"334_CR65","doi-asserted-by":"publisher","unstructured":"Ovadia O, Brief M, Mishaeli M, Elisha O (2024) Fine-tuning or retrieval? Comparing knowledge injection in LLMs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 237\u2013250). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-main.15","DOI":"10.18653\/v1\/2024.emnlp-main.15"},{"key":"334_CR66","doi-asserted-by":"publisher","unstructured":"Parthasarathy VB, Zafar A, Khan A, Shahid A (2024) The ultimate guide to fine-tuning LLMs from basics to breakthroughs: An exhaustive review of technologies, research, best practices, applied research challenges and opportunities (Version 1.0) [arXiv preprint arXiv:2408.13296]. https:\/\/doi.org\/10.48550\/arXiv.2408.13296","DOI":"10.48550\/arXiv.2408.13296"},{"key":"334_CR67","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1162\/tacl_a_00605","volume":"11","author":"O Ram","year":"2023","unstructured":"Ram O, Levine Y, Dalmedigos I, Muhlgay D, Shashua A, Leyton-Brown K, Shoham Y (2023) In-context retrieval-augmented language models. Trans Assoc Comput Linguist 11:1316\u20131331. https:\/\/doi.org\/10.1162\/tacl_a_00605","journal-title":"Trans Assoc Comput Linguist"},{"key":"334_CR68","unstructured":"Rao R (2024). RAG vs fine-tuning: Differences, benefits, and use cases explained. Wevolver. https:\/\/www.wevolver.com\/article\/rag-vs-fine-tuning-differences-benefits-and-use-cases-explained"},{"key":"334_CR69","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Wu T, Guestrin C, Singh S (2020) Beyond accuracy: Behavioural testing of NLP models with CheckList. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4902\u20134912). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.442","DOI":"10.18653\/v1\/2020.acl-main.442"},{"key":"334_CR70","doi-asserted-by":"publisher","unstructured":"Roy SS, Thota P, Naragam KV, Nilizadeh S (2024) From chatbots to phishbots? Phishing scam generation in commercial large language models. In: 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, pp 36\u201354. https:\/\/doi.org\/10.1109\/SP54263.2024.00182","DOI":"10.1109\/SP54263.2024.00182"},{"key":"334_CR71","unstructured":"Sager N, Cabaza T, Cusack M, Bass R, Dominguez J (2024) Rethinking retrieval-augmented fine-tuning in an evolving LLM landscape. SMU Data Science Review, 8(2), Article 2. https:\/\/scholar.smu.edu\/datasciencereview\/vol8\/iss2\/2"},{"key":"334_CR72","doi-asserted-by":"publisher","unstructured":"Sal\u0131c\u0131 M, \u00d6l\u00e7er E (2024) Impact of transformer-based models in NLP: An in-depth study on BERT and GPT. In Proceedings of the 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1\u20136). IEEE. https:\/\/doi.org\/10.1109\/IDAP64064.2024.10710796","DOI":"10.1109\/IDAP64064.2024.10710796"},{"key":"334_CR73","doi-asserted-by":"publisher","unstructured":"Shi Z, Tonolini F, Aletras N, Yilmaz E, Kazai G, Jiao Y (2023) Rethinking semi-supervised learning with language models. In: Findings of the association for computational linguistics: ACL 2023, Toronto, Canada. Association for Computational Linguistics, pp 5614\u20135634. https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.347","DOI":"10.18653\/v1\/2023.findings-acl.347"},{"key":"334_CR74","doi-asserted-by":"publisher","unstructured":"Shi T, Chen K, Zhao J (2024) Safer-Instruct: Aligning language models with automated preference data. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 7636\u20137651). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.422","DOI":"10.18653\/v1\/2024.naacl-long.422"},{"key":"334_CR75","doi-asserted-by":"crossref","unstructured":"Shinn N, Cassano F, Berman E, Gopinath A, Narasimhan K, Yao S (2023) Reflexion: Language agents with verbal reinforcement learning. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates. (Original pre-print: arXiv:2303.11366).","DOI":"10.52202\/075280-0377"},{"key":"334_CR76","doi-asserted-by":"publisher","unstructured":"Shuster K, Poff S, Chen M, Kiela D, Weston J (2021) Retrieval augmentation reduces hallucination in conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 3784\u20133803). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.320","DOI":"10.18653\/v1\/2021.findings-emnlp.320"},{"key":"334_CR77","doi-asserted-by":"publisher","unstructured":"Soudani H, Kanoulas E, Hasibi F (2024) Fine tuning vs. retrieval augmented generation for less popular knowledge. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia-Pacific Region (SIGIR-AP \u201924) (pp. 12\u201322). ACM. https:\/\/doi.org\/10.1145\/3673791.3698415","DOI":"10.1145\/3673791.3698415"},{"key":"334_CR78","doi-asserted-by":"publisher","unstructured":"Sulimov D (2024). Prompt-efficient fine-tuning for GPT-like deep models to reduce hallucination and improve reproducibility in scientific text generation using stochastic optimisation techniques (arXiv Preprint No. 2411.06445). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2411.06445","DOI":"10.48550\/arXiv.2411.06445"},{"key":"334_CR79","doi-asserted-by":"publisher","unstructured":"Sun Y, Wang S, Li Y, Feng S, Chen X, Zhang H, \u2026 Wu H (2019) ERNIE: Enhanced representation through knowledge integration (arXiv Preprint No. 1904.09223). arXiv. https:\/\/doi.org\/10.48550\/arXiv.1904.09223","DOI":"10.48550\/arXiv.1904.09223"},{"key":"334_CR80","doi-asserted-by":"publisher","unstructured":"Sun X, Xie J, Chen Z, Liu Q, Wu S, Chen Y, Song B, Wang W, Wang L (2025) Divide-Then-Align: honest alignment based on the knowledge boundary of RAG. In: Proceedings of ACL 2025. https:\/\/doi.org\/10.18653\/v1\/2025.acl-long.561","DOI":"10.18653\/v1\/2025.acl-long.561"},{"key":"334_CR81","doi-asserted-by":"publisher","unstructured":"Tahir A, Cheng L, Liu H (2024) JORA: JAX tensor-parallel LoRA library for Retrieval-Augmented Fine-Tuning (arXiv Preprint No. 2403.11366). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2403.11366","DOI":"10.48550\/arXiv.2403.11366"},{"key":"334_CR82","doi-asserted-by":"publisher","unstructured":"Thomas DR, Gatz E, Gupta S, Lin J, Tipper C, Koedinger KR (2024) Using generative AI to provide feedback to adult tutors in training and assess real-life performance. In D. Guralnick, M. E. Auer, & A. Poce (Eds.), Creative Approaches to Technology-Enhanced Learning for the Workplace and Higher Education (pp. 204\u2013214). Springer. https:\/\/doi.org\/10.1007\/978-3-031-73427-4_21","DOI":"10.1007\/978-3-031-73427-4_21"},{"key":"334_CR83","doi-asserted-by":"publisher","unstructured":"Truong TH, Baldwin T, Verspoor K, Cohn T (2023) Language models are not naysayers: An analysis of language models on negation benchmarks. In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (\u2605SEM 2023) (pp. 101\u2013114). ACL. https:\/\/doi.org\/10.18653\/v1\/2023.starsem-1.10","DOI":"10.18653\/v1\/2023.starsem-1.10"},{"key":"334_CR84","doi-asserted-by":"publisher","unstructured":"Tural B, \u00d6rpek Z, Destan Z (2024) Retrieval-augmented generation (RAG) and LLM integration. In 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS) (pp. 1\u20135). IEEE. https:\/\/doi.org\/10.1109\/ISAS64331.2024.10845308","DOI":"10.1109\/ISAS64331.2024.10845308"},{"key":"334_CR85","unstructured":"Vidal C, Subramanian S (2024) RAFT: A new way to teach LLMs to be better at RAG. Microsoft Tech Community \u2013 AI Platform Blog. https:\/\/techcommunity.microsoft.com\/blog\/aiplatformblog\/raft-a-new-way-to-teach-llms-to-be-better-at-rag\/4084674"},{"key":"334_CR86","doi-asserted-by":"publisher","unstructured":"Wang R, Li H, Han X, Zhang Y, Baldwin T (2024a) Learning from failure: Integrating negative examples when fine-tuning large language models as agents (arXiv Preprint No. 2402.11651). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2402.11651","DOI":"10.48550\/arXiv.2402.11651"},{"key":"334_CR87","doi-asserted-by":"publisher","unstructured":"Wang Z, Teo SX, Ouyang J et al (2024b) M-RAG: reinforcing large language model performance through retrieval-augmented generation with multiple partitions. In: Proceedings of ACL 2024, pp 2314\u20132330. https:\/\/doi.org\/10.48550\/arXiv.2405.16420","DOI":"10.48550\/arXiv.2405.16420"},{"key":"334_CR88","unstructured":"Wang C, Jiang Y, Yang C, Liu H, Chen Y (2024c) Beyond reverse KL: Generalizing direct preference optimization with diverse divergence constraints. In Proceedings of the Twelfth International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=2cRzmWXK9N"},{"key":"334_CR89","doi-asserted-by":"publisher","unstructured":"Warnakulasuriya S, Hapuarachchi K (2024) From knowledge to action: Leveraging Retrieval-Augmented Fine-Tuning (RAFT) to empower quick and confident first-aid decisions in emergencies (Pre-print on ResearchGate). https:\/\/doi.org\/10.13140\/RG.2.2.35911.30888","DOI":"10.13140\/RG.2.2.35911.30888"},{"key":"334_CR90","doi-asserted-by":"publisher","unstructured":"Wick ML, Liu S, Bartolo M, FitzGerald N, Williams A (2020) Detecting and exorcising statistical demons from language models with anti-models of negative data (arXiv Preprint No. 2010.11855). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2010.11855","DOI":"10.48550\/arXiv.2010.11855"},{"key":"334_CR91","doi-asserted-by":"publisher","unstructured":"Xie Y, Fang M, Pi R, Gong NZ (2024) GradSafe: detecting jailbreak prompts for LLMs via safety-critical gradient analysis. In: Proceedings of ACL 2024, pp 3881\u20133893. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.30","DOI":"10.18653\/v1\/2024.acl-long.30"},{"key":"334_CR92","doi-asserted-by":"publisher","unstructured":"Xie T, Qi X, Zeng Y, Huang Y et al (2025) SORRY-Bench: systematically evaluating large language model safety refusal. In: Proceedings of ICLR 2025. https:\/\/doi.org\/10.48550\/arXiv.2406.14598","DOI":"10.48550\/arXiv.2406.14598"},{"key":"334_CR93","doi-asserted-by":"crossref","unstructured":"Xu S, Pang L, Yu M et al (2024) Unsupervised information refinement training of large language models for retrieval-augmented generation. https:\/\/arxiv.org\/abs\/2402.18150","DOI":"10.18653\/v1\/2024.acl-long.9"},{"key":"334_CR94","doi-asserted-by":"publisher","unstructured":"Xue L, Constant N, Roberts A, Kale M, Al-Rfou R, Siddhant A, \u2026 Raffel C (2021) mT5: a massively multilingual pre-trained text-to-text transformer. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics. ACL, pp 483\u2013498. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.41","DOI":"10.18653\/v1\/2021.naacl-main.41"},{"key":"334_CR95","doi-asserted-by":"crossref","unstructured":"Yang Z, Qi P, Zhang S, Bengio Y, Cohen W, Salakhutdinov R, Manning CD (2018) HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 2369\u20132380). ACL. https:\/\/arxiv.org\/abs\/1809.09600","DOI":"10.18653\/v1\/D18-1259"},{"key":"334_CR96","doi-asserted-by":"crossref","unstructured":"Yu L, Cao M, Cheung JCK, Dong Y (2024) Mechanistic understanding and mitigation of language model non-factual hallucinations. https:\/\/arxiv.org\/abs\/2403.18167","DOI":"10.18653\/v1\/2024.findings-emnlp.466"},{"issue":"5","key":"334_CR97","doi-asserted-by":"publisher","first-page":"856","DOI":"10.3390\/math13050856","volume":"13","author":"W Zhang","year":"2025","unstructured":"Zhang W, Zhang J (2025) Hallucination mitigation for retrieval-augmented large language models: a review. Mathematics 13(5):856","journal-title":"Mathematics"},{"key":"334_CR98","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1162\/tacl_a_00595","volume":"11","author":"X Zhang","year":"2023","unstructured":"Zhang X, Ma X, Shi P, Lin J (2023) Miracl: a multilingual retrieval dataset covering 18 languages. Transactions of the ACL 11:1116\u20131132. https:\/\/doi.org\/10.1162\/tacl_a_00595","journal-title":"Transactions of the ACL"},{"key":"334_CR99","doi-asserted-by":"crossref","unstructured":"Zhang X, Ma X, Shi P, Lin J (2021) Mr.TyDi: A multi-lingual benchmark for dense retrieval. Proceedings of the 1st Workshop on Multilingual Representation Learning (MRL 2021), 127\u2013137. https:\/\/aclanthology.org\/2021.mrl-1.12","DOI":"10.18653\/v1\/2021.mrl-1.12"},{"key":"334_CR100","doi-asserted-by":"publisher","unstructured":"Zhang T, Norouzian A, Mohan A, Ducatelle F (2024a) A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track (pp. 910\u2013919). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-industry.68","DOI":"10.18653\/v1\/2024.emnlp-industry.68"},{"key":"334_CR101","doi-asserted-by":"publisher","unstructured":"Zhang T, Patil SG, Jain N, Shen S, Zaharia M, Stoica I, Gonzalez JE (2024b) RAFT: adapting language model to domain-specific RAG. In: Proceedings of the First Conference on Language Modeling (COLM 2024). (Paper ID 117). https:\/\/doi.org\/10.48550\/arXiv.2403.10131","DOI":"10.48550\/arXiv.2403.10131"},{"key":"334_CR102","doi-asserted-by":"publisher","unstructured":"Zhang X, Peng B, Tian Y, Zhou J, Jin L, Song L, Mi H, Meng HM (2024c) Self-alignment for factuality: mitigating hallucinations in LLMs via self-evaluation. In: Proceedings of ACL 2024, pp 2759\u20132774. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.107","DOI":"10.18653\/v1\/2024.acl-long.107"},{"key":"334_CR103","doi-asserted-by":"publisher","unstructured":"Zhang Y, Li Y, Liu J (2024d) Unified efficient fine-tuning techniques for open-source large language models [ResearchSquare pre-print Version 1]. https:\/\/doi.org\/10.21203\/rs.3.rs-4660140\/v1","DOI":"10.21203\/rs.3.rs-4660140\/v1"},{"key":"334_CR104","doi-asserted-by":"publisher","unstructured":"Zhang Z, Lei L, Wu L, Sun R, Huang Y, Long C, Liu X, Lei X, Tang J, Huang M (2024e) SafetyBench: evaluating the safety of large language models. In: Proceedings of ACL 2024. Association for Computational Linguistics, pp 15537\u201315553. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.830","DOI":"10.18653\/v1\/2024.acl-long.830"},{"key":"334_CR105","doi-asserted-by":"publisher","unstructured":"Zhu R, Jiang X, Wu J, Ma Z, Song J, Bai F, Lin D, Wu L, He C (2025) GRAIT: gradient-driven refusal-aware instruction tuning for effective hallucination mitigation. In: Findings of the association for computational linguistics: NAACL 2025, pp 4006\u20134021. https:\/\/doi.org\/10.18653\/v1\/2025.findings-naacl.223","DOI":"10.18653\/v1\/2025.findings-naacl.223"},{"key":"334_CR106","doi-asserted-by":"publisher","unstructured":"Ziaei R, Schmidgall S (2023) Language models are susceptible to incorrect patient self-diagnosis in medical applications [arXiv pre-print arXiv:2309.09362]. https:\/\/doi.org\/10.48550\/arXiv.230","DOI":"10.48550\/arXiv.230"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00334-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00334-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00334-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:38:49Z","timestamp":1773153529000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00334-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,6]]},"references-count":106,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["334"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00334-6","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,6]]},"assertion":[{"value":"3 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"21"}}