{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T10:53:16Z","timestamp":1782298396262,"version":"3.54.5"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFC2705700"],"award-info":[{"award-number":["2023YFC2705700"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23B2048"],"award-info":[{"award-number":["U23B2048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076186"],"award-info":[{"award-number":["62076186"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225113"],"award-info":[{"award-number":["62225113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tkde.2024.3376453","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T18:35:29Z","timestamp":1710873329000},"page":"4835-4848","source":"Crossref","is-referenced-by-count":13,"title":["PanDa: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0118-5217","authenticated-orcid":false,"given":"Qihuang","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8976-2084","authenticated-orcid":false,"given":"Liang","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3907-8820","authenticated-orcid":false,"given":"Juhua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0059-8458","authenticated-orcid":false,"given":"Bo","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7225-5449","authenticated-orcid":false,"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Computing &#x0026; Data Science, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref2","article-title":"RoBERTa: A robustly optimized bert pretraining approach","author":"Liu","year":"2019"},{"key":"ref3","first-page":"1","article-title":"DeBERTa: Decoding-enhanced BERT with disentangled attention","volume-title":"Proc. Int. Conf. Learn. Representations","author":"He"},{"key":"ref4","first-page":"140:1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref5","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Brown"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401156"},{"key":"ref7","first-page":"1","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hu"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.346"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2023.08.012"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.467"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.290"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.634"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00300"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"ref18","article-title":"Toward efficient language model pretraining and downstream adaptation via self-evolution: A case study on superglue","author":"Zhong","year":"2022"},{"key":"ref19","article-title":"Bag of tricks for effective language model pretraining and downstream adaptation: A case study on glue","author":"Zhong","year":"2023"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.254"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3560815"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2023.3341917"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3028943"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3250499"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3038670"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3119619"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981329"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981314"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-emnlp.300"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.579"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.749"},{"key":"ref32","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Houlsby"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3266495"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.576"},{"key":"ref36","first-page":"390","article-title":"Few-shot text generation with pattern-exploiting training","volume-title":"Proc. Conf. Empir. Methods Natural Lang. Process.","author":"Schick"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2022.11.003"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.446"},{"key":"ref41","first-page":"1","article-title":"Model ensemble instead of prompt fusion: A sample-specific knowledge transfer method for few-shot prompt tuning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Peng"},{"key":"ref42","first-page":"1","article-title":"Distilling the knowledge in a neural network","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hinton"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_34"},{"key":"ref44","first-page":"1602","article-title":"Born again neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Furlanello"},{"key":"ref45","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2024.acl-long.587","article-title":"Revisiting knowledge distillation for autoregressive language models","author":"Zhong","year":"2024"},{"key":"ref46","first-page":"1","article-title":"Understanding and improving lexical choice in non-autoregressive translation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ding"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.26615\/978-954-452-056-4_050"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.603"},{"key":"ref50","first-page":"6691","article-title":"A contrastive cross-channel data augmentation framework for aspect-based sentiment analysis","volume-title":"Proc. 29th Int. Conf. Comput. Linguistics","author":"Wang"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5446"},{"key":"ref52","first-page":"3261","article-title":"SuperGLUE: A stickier benchmark for general-purpose language understanding systems","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1264"},{"key":"ref54","first-page":"142","article-title":"Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition","volume-title":"Proc. Conf. North Amer. Assoc. Comput. Linguistics","author":"Sang"},{"key":"ref55","first-page":"89","article-title":"Introduction to the CoNLL-2004 shared task: Semantic role labeling","volume-title":"Proc. 8th Conf. Comput. Natural Lang. Learn.","author":"Carreras"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.3115\/1614049.1614064"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3115\/1706543.1706571"},{"key":"ref58","first-page":"1","article-title":"CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes","volume-title":"Proc. Conf. Empir. Methods Natural Lang. Process.","author":"Pradhan"},{"key":"ref59","first-page":"6906","article-title":"Does knowledge distillation really work?","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Stanton"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/362"},{"key":"ref61","article-title":"OPT: Open pre-trained transformer language models","author":"Zhang","year":"2022"},{"key":"ref62","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.2307\/1412159"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10629652\/10475529.pdf?arnumber=10475529","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T16:53:52Z","timestamp":1731603232000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10475529\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":63,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2024.3376453","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}