{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T20:14:38Z","timestamp":1780690478551,"version":"3.54.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFA1011900"],"award-info":[{"award-number":["2024YFA1011900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276277"],"award-info":[{"award-number":["62276277"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"in part by Guangdong Basic and Applied Basic Research Foundation","award":["2022B1515120059"],"award-info":[{"award-number":["2022B1515120059"]}]},{"DOI":"10.13039\/501100017688","name":"Guangdong Provincial Key Laboratory of Intellectual Property and Big Data","doi-asserted-by":"publisher","award":["2018B030322016"],"award-info":[{"award-number":["2018B030322016"]}],"id":[{"id":"10.13039\/501100017688","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1109\/tnnls.2025.3635881","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:38:05Z","timestamp":1764873485000},"page":"2563-2576","source":"Crossref","is-referenced-by-count":1,"title":["P\n                    <sup>3<\/sup>\n                    L: Patent Prediction With Prompt Learning"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7440-6851","authenticated-orcid":false,"given":"Yi-Hong","family":"Lu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei-Yuan","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6578-0616","authenticated-orcid":false,"given":"Man-Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan-Tao","family":"Cai","sequence":"additional","affiliation":[{"name":"South China Technology Commercialization Center, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeng-Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"South China Technology Commercialization Center, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang-Yin","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Key Laboratory of Agricultural Products Quality and Safety Traceability Information Technology and the College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7561-3704","authenticated-orcid":false,"given":"Qing-Yun","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5972-559X","authenticated-orcid":false,"given":"Chang-Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-economics-080511-111008"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.51219\/JAIMLD\/premkumar-ganesan\/304"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/701"},{"key":"ref4","article-title":"The Harvard USPTO patent dataset: A large-scale, well-structured, and multi-purpose corpus of patent applications","volume-title":"Proc. NIPS","author":"S\u00fczg\u00fcn"},{"key":"ref5","first-page":"17283","article-title":"Big bird: Transformers for longer sequences","volume-title":"Proc. NIPS","volume":"33","author":"Zaheer"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1212"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143859"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2020.101079"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3177746"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11882"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1002\/int.22846"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102594"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3617680"},{"issue":"140","key":"ref15","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2019.100983"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401066"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3142773"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3282914"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3176409"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3159592"},{"key":"ref22","article-title":"Efficient estimation of word representations in vector space","volume-title":"Proc. ICLR","author":"Mikolov"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3366977"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24818\/ida-ql\/2019.5"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1907.11692"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1139"},{"key":"ref28","first-page":"5754","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","volume-title":"Proc. NIPS","author":"Yang"},{"key":"ref29","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. NIPS","volume":"33","author":"Brown"},{"key":"ref30","article-title":"Training language models to follow instructions with human feedback","volume-title":"Proc. NIPS","author":"Ouyang"},{"key":"ref31","article-title":"GShard: Scaling giant models with conditional computation and automatic sharding","volume-title":"Proc. ICLR","author":"Lepikhin"},{"key":"ref32","article-title":"PaLM: Scaling language modeling with pathways","author":"Chowdhery","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref33","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv:2302.13971"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1525\/9780520940420-020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3649449"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00324"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acllong.353"},{"key":"ref40","article-title":"Response generation with context-aware prompt learning","author":"Gu","year":"2021","journal-title":"arXiv:2111.02643"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.08.002"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612252"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5681"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591738"},{"key":"ref46","article-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024","journal-title":"arXiv:2407.21783"},{"key":"ref47","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. ICLR","author":"Hu"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11549955\/11277339.pdf?arnumber=11277339","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T19:39:50Z","timestamp":1780688390000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11277339\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":47,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2025.3635881","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]}}}