{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:29:24Z","timestamp":1782516564179,"version":"3.54.5"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["No.2020AAA0109400"],"award-info":[{"award-number":["No.2020AAA0109400"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-05934-9","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T05:38:14Z","timestamp":1733463494000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Data augmented large language models for medical record generation"],"prefix":"10.1007","volume":"55","author":[{"given":"Xuanyi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genghong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiguang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4006-2000","authenticated-orcid":false,"given":"Jiren","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"5934_CR1","doi-asserted-by":"crossref","unstructured":"Guan J, Li R, Yu S, Zhang X (2018) Generation of synthetic electronic medical record text. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 374\u2013380. IEEE","DOI":"10.1109\/BIBM.2018.8621223"},{"key":"5934_CR2","unstructured":"Fei F, Qu L, Zhao H (2017) Change it?! 46% of doctors spend nearly 40% of their time writing medical records in daily work, with results newly released by the 20 month\u2019s survey in this magazine, \u201cbasic standards for writing medical records.\u201d China medicine and pharmacy 7(21):1\u20138"},{"key":"5934_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1472-6963-10-94","volume":"10","author":"G Becker","year":"2010","unstructured":"Becker G, Kempf DE, Xander CJ, Momm F, Olschewski M, Blum HE (2010) Four minutes for a patient, twenty seconds for a relative-an observational study at a university hospital. BMC Health Serv Res 10:1\u20139","journal-title":"BMC Health Serv Res"},{"issue":"5","key":"5934_CR4","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1001\/jamainternmed.2022.0372","volume":"182","author":"A Gaffney","year":"2022","unstructured":"Gaffney A, Woolhandler S, Cai C, Bor D, Himmelstein J, McCormick D, Himmelstein DU (2022) Medical documentation burden among us office-based physicians in 2019: a national study. JAMA Intern Med 182(5):564\u2013566","journal-title":"JAMA Intern Med"},{"key":"5934_CR5","doi-asserted-by":"publisher","first-page":"3166","DOI":"10.1007\/s11606-020-06087-4","volume":"35","author":"F Toscano","year":"2020","unstructured":"Toscano F, O\u2019Donnell E, Broderick JE, May M, Tucker P, Unruh MA, Messina G, Casalino LP (2020) How physicians spend their work time: an ecological momentary assessment. J Gen Intern Med 35:3166\u20133172","journal-title":"J Gen Intern Med"},{"key":"5934_CR6","doi-asserted-by":"crossref","unstructured":"Muhiyaddin R, Elfadl A, Mohamed E, Shah Z, Alam T, Abd-Alrazaq A, Househ M (2022) Electronic health records and physician burnout: a scoping review. Informatics and Technology in Clinical Care and Public Health 481\u2013484","DOI":"10.3233\/SHTI210962"},{"issue":"1","key":"5934_CR7","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1038\/s41467-024-45563-x","volume":"15","author":"J Dagdelen","year":"2024","unstructured":"Dagdelen J, Dunn A, Lee S, Walker N, Rosen AS, Ceder G, Persson KA, Jain A (2024) Structured information extraction from scientific text with large language models. Nat Commun 15(1):1418","journal-title":"Nat Commun"},{"key":"5934_CR8","doi-asserted-by":"crossref","unstructured":"Huang D, Wei Z, Yue A, Zhao X, Chen Z, Li R, Jiang K, Chang B, Zhang Q, Zhang S (2023) Dsqa-llm: Domain-specific intelligent question answering based on large language model. In: International Conference on AI-generated Content, pp. 170\u2013180. Springer","DOI":"10.1007\/978-981-99-7587-7_14"},{"key":"5934_CR9","doi-asserted-by":"crossref","unstructured":"Su Y, Vandyke D, Wang S, Fang Y, Collier N (2021) Plan-then-generate: Controlled data-to-text generation via planning. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 895\u2013909","DOI":"10.18653\/v1\/2021.findings-emnlp.76"},{"key":"5934_CR10","doi-asserted-by":"crossref","unstructured":"Schaik TA, Pugh B (2024) A field guide to automatic evaluation of llm-generated summaries. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2832\u20132836","DOI":"10.1145\/3626772.3661346"},{"key":"5934_CR11","doi-asserted-by":"crossref","unstructured":"Bao K, Zhang J, Zhang Y, Wenjie W, Feng F, He X (2023) Large language models for recommendation: Progresses and future directions. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pp. 306\u2013309","DOI":"10.1145\/3624918.3629550"},{"key":"5934_CR12","doi-asserted-by":"crossref","unstructured":"Fleming SL, Lozano A, Haberkorn WJ, Jindal JA, Reis E, Thapa R, Blankemeier L, Genkins JZ, Steinberg E, Nayak A (2024) Medalign: A clinician-generated dataset for instruction following with electronic medical records. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 22021\u201322030","DOI":"10.1609\/aaai.v38i20.30205"},{"key":"5934_CR13","doi-asserted-by":"crossref","unstructured":"Peng J, Ni P, Zhu J, Dai Z, Li Y, Li G, Bai X (2019) Automatic generation of electronic medical record based on gpt2 model. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 6180\u20136182. IEEE","DOI":"10.1109\/BigData47090.2019.9006414"},{"key":"5934_CR14","doi-asserted-by":"crossref","unstructured":"Nievas M, Basu A, Wang Y, Singh H (2024) Distilling large language models for matching patients to clinical trials. Journal of the American Medical Informatics Association 073","DOI":"10.1093\/jamia\/ocae073"},{"key":"5934_CR15","unstructured":"Zhang Y, Li Y, Cui L, Cai D, Liu L, Fu T, Huang X, Zhao E, Zhang Y, Chen Y et al (2023) Siren\u2019s song in the ai ocean: A survey on hallucination in large language models. arXiv:2309.01219"},{"key":"5934_CR16","unstructured":"Bai J, Bai S, Chu Y, Cui Z, Dang K, Deng X, Fan Y, Ge W, Han Y, Huang F et al (2023) Qwen technical report. arXiv:2309.16609"},{"key":"5934_CR17","unstructured":"Bao Z, Chen W, Xiao S, Ren K, Wu J, Zhong C, Peng J, Huang X, Wei Z (2023) Disc-medllm: Bridging general large language models and real-world medical consultation. arXiv:2308.14346"},{"issue":"6","key":"5934_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3653304","volume":"18","author":"J Yang","year":"2024","unstructured":"Yang J, Jin H, Tang R, Han X, Feng Q, Jiang H, Zhong S, Yin B, Hu X (2024) Harnessing the power of llms in practice: A survey on chatgpt and beyond. ACM Trans Knowl Discov Data 18(6):1\u201332","journal-title":"ACM Trans Knowl Discov Data"},{"key":"5934_CR19","doi-asserted-by":"crossref","unstructured":"Wang J, Xu Z, Wang X, Zhao Y, Liu G, Tian R, Jing L (2020) Design of integrated magnetic transformer for high frequency llc converter. In: 2020 4th International Conference on HVDC (HVDC), pp. 986\u2013991. IEEE","DOI":"10.1109\/HVDC50696.2020.9292690"},{"key":"5934_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104258","volume":"131","author":"Q-T Ho","year":"2021","unstructured":"Ho Q-T, Le NQK, Ou Y-Y (2021) Fad-bert: improved prediction of fad binding sites using pre-training of deep bidirectional transformers. Comput Biol Med 131:104258","journal-title":"Comput Biol Med"},{"key":"5934_CR21","doi-asserted-by":"crossref","unstructured":"Choi H, Kim J, Joe S, Gwon Y (2021) Evaluation of bert and albert sentence embedding performance on downstream nlp tasks. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5482\u20135487. IEEE","DOI":"10.1109\/ICPR48806.2021.9412102"},{"key":"5934_CR22","doi-asserted-by":"crossref","unstructured":"Zhu M, Song Y, Jin G, Jiang K (2020) Identifying personal experience tweets of medication effects using pre-trained roberta language model and its updating. In: Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pp. 127\u2013137","DOI":"10.18653\/v1\/2020.louhi-1.14"},{"key":"5934_CR23","doi-asserted-by":"publisher","first-page":"75144","DOI":"10.1109\/ACCESS.2022.3189956","volume":"10","author":"F Gargiulo","year":"2022","unstructured":"Gargiulo F, Minutolo A, Guarasci R, Damiano E, De Pietro G, Fujita H, Esposito M (2022) An electra-based model for neural coreference resolution. IEEE Access 10:75144\u201375157","journal-title":"IEEE Access"},{"issue":"10","key":"5934_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3624557","volume":"22","author":"HT Duong","year":"2023","unstructured":"Duong HT, Ho VH, Do P (2023) Fact-checking vietnamese information using knowledge graph, datalog, and kg-bert. ACM Transactions on Asian and Low-Resource Language Information Processing 22(10):1\u201323","journal-title":"ACM Transactions on Asian and Low-Resource Language Information Processing"},{"issue":"1","key":"5934_CR25","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","volume":"34","author":"J Li","year":"2020","unstructured":"Li J, Sun A, Han J, Li C (2020) A survey on deep learning for named entity recognition. IEEE Trans Knowl Data Eng 34(1):50\u201370","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"5934_CR26","first-page":"5485","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21(1):5485\u20135551","journal-title":"The Journal of Machine Learning Research"},{"key":"5934_CR27","unstructured":"Tay Y, Dehghani M, Tran VQ, Garcia X, Wei J, Wang X, Chung HW, Bahri D, Schuster T, Zheng S (2022) Ul2: Unifying language learning paradigms. In: The Eleventh International Conference on Learning Representations"},{"key":"5934_CR28","doi-asserted-by":"crossref","unstructured":"Ma G, Wang W, Li Y, Yang Y, Du B, Fu H (2023) Lae-st-moe: Boosted language-aware encoder using speech translation auxiliary task for e2e code-switching asr. In: 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 1\u20138. IEEE","DOI":"10.1109\/ASRU57964.2023.10389662"},{"key":"5934_CR29","doi-asserted-by":"crossref","unstructured":"Du Z, Qian Y, Liu X, Ding M, Qiu J, Yang Z, Tang J (2022) Glm: General language model pretraining with autoregressive blank infilling. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 320\u2013335","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"5934_CR30","doi-asserted-by":"crossref","unstructured":"Xue L, Constant N, Roberts A, Kale M, Al-Rfou R, Siddhant A, Barua A, 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: Human Language Technologies, pp. 483\u2013498","DOI":"10.18653\/v1\/2021.naacl-main.41"},{"key":"5934_CR31","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"5934_CR32","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A (2022) Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst 35:27730\u201327744","journal-title":"Adv Neural Inf Process Syst"},{"key":"5934_CR33","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F et al (2023) Llama: Open and efficient foundation language models. arXiv:2302.13971 (2023)"},{"key":"5934_CR34","unstructured":"Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S et al (2023) Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288"},{"issue":"2","key":"5934_CR35","first-page":"149","volume":"19","author":"B Ji","year":"2019","unstructured":"Ji B, Liu R, Li S, Yu J, Wu Q, Tan Y, Wu J (2019) A hybrid approach for named entity recognition in chinese electronic medical record. BMC Med Inform Decis Mak 19(2):149\u2013158","journal-title":"BMC Med Inform Decis Mak"},{"issue":"3","key":"5934_CR36","doi-asserted-by":"publisher","first-page":"14830","DOI":"10.2196\/14830","volume":"7","author":"F Li","year":"2019","unstructured":"Li F, Jin Y, Liu W, Rawat BPS, Cai P, Yu H (2019) Fine-tuning bidirectional encoder representations from transformers (bert)-based models on large-scale electronic health record notes: an empirical study. JMIR Med Inform 7(3):14830","journal-title":"JMIR Med Inform"},{"key":"5934_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11894-020-00777-z","volume":"22","author":"Y Ahmed","year":"2020","unstructured":"Ahmed Y, Othman M (2020) Emr\/esd: techniques, complications, and evidence. Curr Gastroenterol Rep 22:1\u201312","journal-title":"Curr Gastroenterol Rep"},{"key":"5934_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101771","volume":"102","author":"ZB Miled","year":"2020","unstructured":"Miled ZB, Haas K, Black CM, Khandker RK, Chandrasekaran V, Lipton R, Boustani MA (2020) Predicting dementia with routine care emr data. Artif Intell Med 102:101771","journal-title":"Artif Intell Med"},{"issue":"1","key":"5934_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3445965","volume":"54","author":"Z Nasar","year":"2021","unstructured":"Nasar Z, Jaffry SW, Malik MK (2021) Named entity recognition and relation extraction: State-of-the-art. ACM Computing Surveys (CSUR) 54(1):1\u201339","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"5934_CR40","doi-asserted-by":"publisher","first-page":"73729","DOI":"10.1109\/ACCESS.2019.2920708","volume":"7","author":"D Kim","year":"2019","unstructured":"Kim D, Lee J, So CH, Jeon H, Jeong M, Choi Y, Yoon W, Sung M, Kang J (2019) A neural named entity recognition and multi-type normalization tool for biomedical text mining. IEEE Access 7:73729\u201373740","journal-title":"IEEE Access"},{"key":"5934_CR41","doi-asserted-by":"crossref","unstructured":"Zhao S, Liu T, Zhao S, Wang F (2019) A neural multi-task learning framework to jointly model medical named entity recognition and normalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 817\u2013824","DOI":"10.1609\/aaai.v33i01.3301817"},{"key":"5934_CR42","doi-asserted-by":"crossref","unstructured":"Fu TJ, Li PH, Ma WY (2019) Graphrel: Modeling text as relational graphs for joint entity and relation extraction. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1409\u20131418","DOI":"10.18653\/v1\/P19-1136"},{"key":"5934_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101817","volume":"103","author":"L Li","year":"2020","unstructured":"Li L, Wang P, Yan J, Wang Y, Li S, Jiang J, Sun Z, Tang B, Chang T-H, Wang S (2020) Real-world data medical knowledge graph: construction and applications. Artif Intell Med 103:101817","journal-title":"Artif Intell Med"},{"key":"5934_CR44","doi-asserted-by":"crossref","unstructured":"Wang Z, Sun J (2022) Promptehr: Conditional electronic healthcare records generation with prompt learning. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 2873\u20132885","DOI":"10.18653\/v1\/2022.emnlp-main.185"},{"key":"5934_CR45","doi-asserted-by":"crossref","unstructured":"Jin H, Che H, Lin Y, Chen H (2024) Promptmrg: Diagnosis-driven prompts for medical report generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 2607\u20132615","DOI":"10.1609\/aaai.v38i3.28038"},{"key":"5934_CR46","unstructured":"Chen C, Liu K, Chen Z, Gu Y, Wu Y, Tao M, Fu Z, Ye J (2024) Inside: Llms\u2019 internal states retain the power of hallucination detection. In: The Twelfth International Conference on Learning Representations"},{"key":"5934_CR47","doi-asserted-by":"crossref","unstructured":"Min S, Krishna K, Lyu X, Lewis M, Yih Wt, Koh P, Iyyer M, Zettlemoyer L, Hajishirzi H (2023) Factscore: Fine-grained atomic evaluation of factual precision in long form text generation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 12076\u201312100","DOI":"10.18653\/v1\/2023.emnlp-main.741"},{"issue":"3","key":"5934_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3641289","volume":"15","author":"Y Chang","year":"2024","unstructured":"Chang Y, Wang X, Wang J, Wu Y, Yang L, Zhu K, Chen H, Yi X, Wang C, Wang Y (2024) A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology 15(3):1\u201345","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"5934_CR49","unstructured":"Waldendorf J, Haddow B, Birch A (2024) Contrastive decoding reduces hallucinations in large multilingual machine translation models. In: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2526\u20132539"},{"key":"5934_CR50","doi-asserted-by":"crossref","unstructured":"Maynez J, Narayan S, Bohnet B, McDonald R (2020) On faithfulness and factuality in abstractive summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, p. 1906. Association for Computational Linguistics","DOI":"10.18653\/v1\/2020.acl-main.173"},{"key":"5934_CR51","doi-asserted-by":"crossref","unstructured":"Tang L, Shalyminov I, Wong A, Burnsky J, Vincent J, Singh S, Feng S, Song H, Su H, Sun L (2024) Tofueval: Evaluating hallucinations of llms on topic-focused dialogue summarization. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 4455\u20134480","DOI":"10.18653\/v1\/2024.naacl-long.251"},{"key":"5934_CR52","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1162\/tacl_a_00638","volume":"12","author":"NF Liu","year":"2024","unstructured":"Liu NF, Lin K, Hewitt J, Paranjape A, Bevilacqua M, Petroni F, Liang P (2024) Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics 12:157\u2013173","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"5934_CR53","unstructured":"Shi F, Chen X, Misra K, Scales N, Dohan D, Chi EH, Sch\u00e4rli N, Zhou D (2023) Large language models can be easily distracted by irrelevant context. In: International Conference on Machine Learning, pp. 31210\u201331227. PMLR"},{"issue":"12","key":"5934_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571730","volume":"55","author":"Z Ji","year":"2023","unstructured":"Ji Z, Lee N, Frieske R, Yu T, Su D, Xu Y, Ishii E, Bang YJ, Madotto A, Fung P (2023) Survey of hallucination in natural language generation. ACM Comput Surv 55(12):1\u201338","journal-title":"ACM Comput Surv"},{"key":"5934_CR55","first-page":"79155","volume":"36","author":"G Penedo","year":"2023","unstructured":"Penedo G, Malartic Q, Hesslow D, Cojocaru R, Alobeidli H, Cappelli A, Pannier B, Almazrouei E, Launay J (2023) The refinedweb dataset for falcon llm: Outperforming curated corpora with web data only. Adv Neural Inf Process Syst 36:79155\u201379172","journal-title":"Adv Neural Inf Process Syst"},{"key":"5934_CR56","doi-asserted-by":"crossref","unstructured":"Wang C, Sennrich R (2020) On exposure bias, hallucination and domain shift in neural machine translation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3544\u20133552","DOI":"10.18653\/v1\/2020.acl-main.326"},{"key":"5934_CR57","doi-asserted-by":"crossref","unstructured":"Lin S, Hilton J, Evans O (2022) Truthfulqa: Measuring how models mimic human falsehoods. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3214\u20133252","DOI":"10.18653\/v1\/2022.acl-long.229"},{"key":"5934_CR58","unstructured":"Zhou C, Liu P, Xu P, Iyer S, Sun J, Mao Y, Ma X, Efrat A, Yu P, Yu, L et al (2024) Lima: Less is more for alignment. Advances in Neural Information Processing Systems 36"},{"key":"5934_CR59","unstructured":"Chen L, Li S, Yan J, Wang H, Gunaratna K, Yadav V, Tang Z, Srinivasan V, Zhou T, Huang H et al (2023) Alpagasus: Training a better alpaca with fewer data. In: The Twelfth International Conference on Learning Representations"},{"key":"5934_CR60","unstructured":"Lee A, Hunter C, Ruiz N (2023) Platypus: Quick, cheap, and powerful refinement of llms. In: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following"},{"key":"5934_CR61","unstructured":"Dettmers T, Pagnoni A, Holtzman A, Zettlemoyer L (2024) Qlora: Efficient finetuning of quantized llms. Advances in Neural Information Processing Systems 36"},{"key":"5934_CR62","doi-asserted-by":"crossref","unstructured":"Qin G, Eisner J (2021) Learning how to ask: Querying lms with mixtures of soft prompts. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5203\u20135212","DOI":"10.18653\/v1\/2021.naacl-main.410"},{"key":"5934_CR63","doi-asserted-by":"crossref","unstructured":"Lester B, Al-Rfou R, Constant N (2021) The power of scale for parameter-efficient prompt tuning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 3045\u20133059","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"5934_CR64","doi-asserted-by":"crossref","unstructured":"Li XL, Liang P (2021) Prefix-tuning: Optimizing continuous prompts for generation. In: 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), pp. 4582\u20134597","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"5934_CR65","unstructured":"Chen J, Zhang A, Shi X, Li M, Smola A, Yang D (2023) Parameter-efficient fine-tuning design spaces. In: The Eleventh International Conference on Learning Representations"},{"key":"5934_CR66","first-page":"1950","volume":"35","author":"H Liu","year":"2022","unstructured":"Liu H, Tam D, Muqeeth M, Mohta J, Huang T, Bansal M, Raffel CA (2022) Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. Adv Neural Inf Process Syst 35:1950\u20131965","journal-title":"Adv Neural Inf Process Syst"},{"key":"5934_CR67","unstructured":"Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, De\u00a0Laroussilhe Q, Gesmundo A, Attariyan M, Gelly S (2019) Parameter-efficient transfer learning for nlp. In: International Conference on Machine Learning, pp. 2790\u20132799. PMLR"},{"key":"5934_CR68","doi-asserted-by":"crossref","unstructured":"Zaken EB, Goldberg Y, Ravfogel S (2022) Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 1\u20139","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"5934_CR69","unstructured":"Hu EJ, Wallis P, Allen-Zhu Z, Li Y, Wang S, Wang L, Chen W et al (2021) Lora: Low-rank adaptation of large language models. In: International Conference on Learning Representations"},{"issue":"70","key":"5934_CR70","first-page":"1","volume":"25","author":"HW Chung","year":"2024","unstructured":"Chung HW, Hou L, Longpre S, Zoph B, Tay Y, Fedus W, Li Y, Wang X, Dehghani M, Brahma S (2024) Scaling instruction-finetuned language models. J Mach Learn Res 25(70):1\u201353","journal-title":"J Mach Learn Res"},{"key":"5934_CR71","doi-asserted-by":"crossref","unstructured":"Xu H, Chen Y, Du Y, Shao N, Yanggang W, Li H, Yang Z (2022) Zeroprompt: Scaling prompt-based pretraining to 1,000 tasks improves zero-shot generalization. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 4235\u20134252","DOI":"10.18653\/v1\/2022.findings-emnlp.312"},{"key":"5934_CR72","unstructured":"Lu K, Yuan H, Yuan Z, Lin R, Lin J, Tan C, Zhou C, Zhou J (2023) # instag: Instruction tagging for analyzing supervised fine-tuning of large language models. In: The Twelfth International Conference on Learning Representations"},{"key":"5934_CR73","unstructured":"Longpre S, Hou L, Vu T, Webson A, Chung HW, Tay Y, Zhou D, Le QV, Zoph B, Wei J (2023) The flan collection: Designing data and methods for effective instruction tuning. In: International Conference on Machine Learning, pp. 22631\u201322648. PMLR"},{"key":"5934_CR74","unstructured":"Rafailov R, Sharma A, Mitchell E, Manning CD, Ermon S, Finn C (2024) Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36"},{"key":"5934_CR75","unstructured":"Wang Y, He H, Tan X (2020) Truly proximal policy optimization. In: Uncertainty in Artificial Intelligence, pp. 113\u2013122. PMLR"},{"key":"5934_CR76","unstructured":"Schulman J (2023) Reinforcement learning from human feedback: progress and challenges. In: Berkley Electrical Engineering and Computer Sciences. https:\/\/eecs.Berkeley.Edu\/research\/colloquium\/230419 [accessed 2023-11-15]"},{"key":"5934_CR77","doi-asserted-by":"crossref","unstructured":"Ma X, Gong Y, He P, Zhao H, Duan N (2023) Query rewriting in retrieval-augmented large language models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 5303\u20135315","DOI":"10.18653\/v1\/2023.emnlp-main.322"},{"issue":"3","key":"5934_CR78","first-page":"373","volume":"10","author":"P Kavehzadeh","year":"2022","unstructured":"Kavehzadeh P, Abdollah Pour M, Momtazi S (2022) A transformer-based approach for persian text chunking. Journal of AI and Data Mining 10(3):373\u2013383","journal-title":"Journal of AI and Data Mining"},{"key":"5934_CR79","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, K\u00fcttler H, Lewis M, Yih W-T, Rockt\u00e4schel T (2020) Retrieval-augmented generation for knowledge-intensive nlp tasks. Adv Neural Inf Process Syst 33:9459\u20139474","journal-title":"Adv Neural Inf Process Syst"},{"key":"5934_CR80","first-page":"25968","volume":"34","author":"D Singh","year":"2021","unstructured":"Singh D, Reddy S, Hamilton W, Dyer C, Yogatama D (2021) End-to-end training of multi-document reader and retriever for open-domain question answering. Adv Neural Inf Process Syst 34:25968\u201325981","journal-title":"Adv Neural Inf Process Syst"},{"issue":"240","key":"5934_CR81","first-page":"1","volume":"24","author":"A Chowdhery","year":"2023","unstructured":"Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, Barham P, Chung HW, Sutton C, Gehrmann S (2023) Palm: Scaling language modeling with pathways. J Mach Learn Res 24(240):1\u2013113","journal-title":"J Mach Learn Res"},{"issue":"251","key":"5934_CR82","first-page":"1","volume":"24","author":"G Izacard","year":"2023","unstructured":"Izacard G, Lewis P, Lomeli M, Hosseini L, Petroni F, Schick T, Dwivedi-Yu J, Joulin A, Riedel S, Grave E (2023) Atlas: Few-shot learning with retrieval augmented language models. J Mach Learn Res 24(251):1\u201343","journal-title":"J Mach Learn Res"},{"key":"5934_CR83","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Xia F, Chi E, Le QV, Zhou D (2022) Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process Syst 35:24824\u201324837","journal-title":"Adv Neural Inf Process Syst"},{"key":"5934_CR84","doi-asserted-by":"crossref","unstructured":"Zhang G, Lu X, Tan J, Li J, Zhang Z, Li Q, Hu X (2021) Refinemask: Towards high-quality instance segmentation with fine-grained features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6861\u20136869","DOI":"10.1109\/CVPR46437.2021.00679"},{"key":"5934_CR85","unstructured":"Mandal A, Khan IK, Kumar PS (2019) Query rewriting using automatic synonym extraction for e-commerce search. In: eCOM@ SIGIR"},{"key":"5934_CR86","doi-asserted-by":"crossref","unstructured":"Li S, Lv F, Jin T, Li G, Zheng Y, Zhuang T, Liu Q, Zeng X, Kwok J, Ma Q (2022) Query rewriting in taobao search. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 3262\u20133271","DOI":"10.1145\/3511808.3557068"},{"key":"5934_CR87","doi-asserted-by":"crossref","unstructured":"Qiu Y, Zhang K, Zhang H, Wang S, Xu S, Xiao Y, Long B, Yang WY (2021) Query rewriting via cycle-consistent translation for e-commerce search. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2435\u20132446. IEEE","DOI":"10.1109\/ICDE51399.2021.00276"},{"key":"5934_CR88","unstructured":"Wang Y, Lu H, Xu Y, Goutam R, Song Y, Yin B (2021) Queen: Neural query rewriting in e-commerce"},{"key":"5934_CR89","doi-asserted-by":"crossref","unstructured":"Mohankumar AK, Begwani N, Singh A (2021) Diversity driven query rewriting in search advertising. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3423\u20133431","DOI":"10.1145\/3447548.3467202"},{"key":"5934_CR90","doi-asserted-by":"crossref","unstructured":"Manchanda S, Sharma M, Karypis G (2019) Intent term weighting in e-commerce queries. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2345\u20132348","DOI":"10.1145\/3357384.3358151"},{"key":"5934_CR91","doi-asserted-by":"crossref","unstructured":"Song Z, Chen J, Zhou H, Li L (2021) Triangular bidword generation for sponsored search auction. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 707\u2013715","DOI":"10.1145\/3437963.3441819"},{"key":"5934_CR92","doi-asserted-by":"crossref","unstructured":"Agrawal S, Merugu S, Sembium V (2023) Enhancing e-commerce product search through reinforcement learning-powered query reformulation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 4488\u20134494","DOI":"10.1145\/3583780.3615474"},{"key":"5934_CR93","doi-asserted-by":"crossref","unstructured":"Wang S, Scells H, Koopman B, Zuccon G (2023) Can chatgpt write a good boolean query for systematic review literature search? In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1426\u20131436","DOI":"10.1145\/3539618.3591703"},{"key":"5934_CR94","unstructured":"Theja R (2023) Evaluating the Ideal Chunk Size for a RAG System Using LlamaIndex. https:\/\/www.llamaindex.ai\/blog\/evaluating-the-ideal-chunk-size-for-a-rag-system-using-llamaindex-6207e5d3fec5 Accessed 2023"},{"key":"5934_CR95","unstructured":"Langchain (2023) Recursively Split by Character. https:\/\/python.langchain.com\/docs\/modules\/data_connection\/document_transformers\/recursive_text_splitter Accessed 2023"},{"key":"5934_CR96","unstructured":"Schick T, Dwivedi-Yu J, Dess\u00ec R, Raileanu R, Lomeli M, Hambro E, Zettlemoyer L, Cancedda N, Scialom T (2024) Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems 36"},{"key":"5934_CR97","unstructured":"Yu W, Iter D, Wang S, Xu Y, Ju M, Sanyal S, Zhu C, Zeng M, Jiang M (2023) Generate rather than retrieve: Large language models are strong context generators. In: International Conference on Learning Representations"},{"key":"5934_CR98","doi-asserted-by":"crossref","unstructured":"Cheng D, Huang S, Bi J, Zhan Y, Liu J, Wang Y, Sun H, Wei F, Deng W, Zhang Q (2023) Uprise: Universal prompt retrieval for improving zero-shot evaluation. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 12318\u201312337","DOI":"10.18653\/v1\/2023.emnlp-main.758"},{"key":"5934_CR99","unstructured":"Sun Z, Wang X, Tay Y, Yang Y, Zhou D (2022) Recitation-augmented language models. In: The Eleventh International Conference on Learning Representations"},{"key":"5934_CR100","doi-asserted-by":"crossref","unstructured":"Gao L, Ma X, Lin J, Callan J (2023) Precise zero-shot dense retrieval without relevance labels. In: The 61st Annual Meeting Of The Association For Computational Linguistics","DOI":"10.18653\/v1\/2023.acl-long.99"},{"key":"5934_CR101","doi-asserted-by":"crossref","unstructured":"Hashimoto TB, Zhang H, Liang P (2019) Unifying human and statistical evaluation for natural language generation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1689\u20131701","DOI":"10.18653\/v1\/N19-1169"},{"key":"5934_CR102","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2020.101151","volume":"67","author":"C Lee","year":"2021","unstructured":"Lee C, Gatt A, Miltenburg E, Krahmer E (2021) Human evaluation of automatically generated text: Current trends and best practice guidelines. Computer Speech & Language 67:101151","journal-title":"Computer Speech & Language"},{"key":"5934_CR103","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"5934_CR104","unstructured":"Lin CY (2004) Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381"},{"key":"5934_CR105","unstructured":"Hanna M, Bojar O (2021) A fine-grained analysis of bertscore. In: Proceedings of the Sixth Conference on Machine Translation, pp. 507\u2013517"},{"key":"5934_CR106","unstructured":"Xiao S, Liu Z, Zhang P, Muennighof N (2023) C-pack: Packaged resources to advance general chinese embedding. arXiv:2309.07597"},{"key":"5934_CR107","doi-asserted-by":"crossref","unstructured":"Chen Z, Wu J, Wang W, Su W, Chen G, Xing S, Zhong M, Zhang Q, Zhu X, Lu L (2024) Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24185\u201324198","DOI":"10.1109\/CVPR52733.2024.02283"},{"key":"5934_CR108","unstructured":"Qwen2 (2024) Hello, Qwen2. https:\/\/qwenlm.github.io\/zh\/blog\/qwen2\/ Accessed 2024"},{"key":"5934_CR109","unstructured":"Yang A, Xiao B, Wang B, Zhang B, Bian C, Yin C, Lv C, Pan D, Wang D, Yan D et al (2023) Baichuan 2: Open large-scale language models. arXiv:2309.10305"},{"key":"5934_CR110","unstructured":"Dettmers T, Pagnoni A, Holtzman A, Zettlemoyer L (2023) Qlora: Efficient finetuning of quantized llms. arXiv:2305.14314"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05934-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05934-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05934-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T15:04:34Z","timestamp":1737385474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05934-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":110,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["5934"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05934-9","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"assertion":[{"value":"14 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"All the data is used for research only and will not be used for any other purpose, approved by ethical approval number: 20200818YG, from Medical Ethics Committe of Liaoning Cancer Hospital & Institute. We use and process data based on the principles of Transparency, Innovation, Respect and Security.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}],"article-number":"88"}}