{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T03:03:28Z","timestamp":1781665408731,"version":"3.54.5"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022ZD0160502"],"award-info":[{"award-number":["2022ZD0160502"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61925601"],"award-info":[{"award-number":["61925601"]}],"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":["62006138"],"award-info":[{"award-number":["62006138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Social Science Fund of China","award":["20&ZD279"],"award-info":[{"award-number":["20&ZD279"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE\/ACM Trans. Audio Speech Lang. Process."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/taslp.2024.3407519","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T17:34:22Z","timestamp":1717781662000},"page":"3002-3013","source":"Crossref","is-referenced-by-count":9,"title":["Black-Box Prompt Tuning With Subspace Learning"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6224-8739","authenticated-orcid":false,"given":"Yuanhang","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2426-6220","authenticated-orcid":false,"given":"Zhixing","family":"Tan","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1374-5979","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3087-242X","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Brown","year":"2020"},{"issue":"140","key":"ref2","first-page":"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":"ref3","first-page":"4582","article-title":"Prefix-tuning: Optimizing continuous prompts for generation","volume-title":"Proc. Annu. Meeting Assoc. Comput. Linguistics 11th Int. Joint Conf. Natural Lang. Process.","author":"Li","year":"2021"},{"key":"ref4","first-page":"3045","article-title":"The power of scale for parameter-efficient prompt tuning","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Lester","year":"2021"},{"key":"ref5","first-page":"20841","article-title":"Black-box tuning for language-model-as-a-service","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sun","year":"2022"},{"key":"ref6","first-page":"3916","article-title":"BBTv2: Towards a gradient-free future with large language models","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Sun","year":"2022"},{"key":"ref7","first-page":"6572","article-title":"Neural ordinary differential equations","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Chen","year":"2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898718768"},{"key":"ref9","article-title":"Exploring low-dimensional intrinsic task subspace via prompt tuning","author":"Qin","year":"2021"},{"key":"ref10","first-page":"5039","article-title":"SPoT: Better frozen model adaptation through soft prompt transfer","volume-title":"Proc. Annu. Meeting Assoc. Comput. Linguistics","author":"Vu","year":"2022"},{"key":"ref11","first-page":"3949","article-title":"On transferability of prompt tuning for natural language processing","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Su","year":"2022"},{"key":"ref12","first-page":"2339","article-title":"Its not just size that matters: Small language models are also few-shot learners","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Schick","year":"2021"},{"key":"ref13","first-page":"4222","article-title":"Autoprompt: Eliciting knowledge from language models with automatically generated prompts","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Shin","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.295"},{"issue":"2","key":"ref15","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1162\/106365601750190398","article-title":"Completely derandomized self-adaptation in evolution strategies","volume":"9","author":"Hansen","year":"2001","journal-title":"Evol. Comput."},{"issue":"1","key":"ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/106365603321828970","article-title":"Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)","volume":"11","author":"Hansen","year":"2003","journal-title":"Evol. Comput."},{"issue":"1","key":"ref17","first-page":"949","article-title":"Natural evolution strategies","volume":"15","author":"Wierstra","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"ref19","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref20","first-page":"1631","article-title":"Recursive deep models for semantic compositionality over a sentiment treebank","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Socher","year":"2013"},{"key":"ref21","first-page":"502","article-title":"SemEval-2017 task 4: Sentiment analysis in twitter","volume-title":"Proc. Int. Workshop Semantic Eval.","author":"Rosenthal","year":"2017"},{"key":"ref22","first-page":"142","article-title":"Learning word vectors for sentiment analysis","volume-title":"Proc. Annu. Meeting Assoc. Comput. Linguistics","author":"Maas","year":"2011"},{"key":"ref23","first-page":"649","article-title":"Character-level convolutional networks for text classification","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Zhang","year":"2015"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-005-7880-9"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014073"},{"key":"ref26","first-page":"115","article-title":"Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales","volume-title":"Proc. Annu. Meeting Assoc. Comput. Linguistics","author":"Pang","year":"2005"},{"key":"ref27","first-page":"124","article-title":"The WebNLG challenge: Generating text from RDF data","volume-title":"Proc. Int. Conf. Natural Lang. Gener.","author":"Gardent","year":"2017"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/w17-5525"},{"key":"ref29","first-page":"311","article-title":"Bleu: A method for automatic evaluation of machine translation","volume-title":"Proc. Annu. Meeting Assoc. Comput. Linguistics","author":"Papineni","year":"2002"},{"key":"ref30","first-page":"65","article-title":"METEOR: An automatic metric for MT evaluation with improved correlation with human judgments","volume-title":"Proc. ACL Workshop Intrinsic Extrinsic Eval. Measures Mach. Transl. Summarization","author":"Banerjee","year":"2005"},{"key":"ref31","first-page":"74","article-title":"ROUGE: A package for automatic evaluation of summaries","volume-title":"Proc. Workshop Text Summarization Branches Out","author":"Lin","year":"2004"},{"key":"ref32","first-page":"38","article-title":"Transformers: State-of-the-art natural language processing","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.: System Demonstrations","author":"Wolf","year":"2020"},{"key":"ref33","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Devlin","year":"2019"},{"key":"ref34","article-title":"Language models are unsupervised multitask learners","author":"Radford","year":"2019"},{"key":"ref35","article-title":"ADAM: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2015"},{"key":"ref36","article-title":"Black-box adversarial attack with transferable model-based embedding","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Huang","year":"2020"},{"key":"ref37","first-page":"3622","article-title":"Meta-learning for low-resource neural machine translation","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Gu","year":"2018"},{"key":"ref38","first-page":"632","article-title":"A large annotated corpus for learning natural language inference","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process. Assoc. Comput. Linguistics","author":"Bowman","year":"2015"},{"key":"ref39","first-page":"1112","article-title":"A broad-coverage challenge corpus for sentence understanding through inference","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Williams","year":"2018"},{"key":"ref40","first-page":"2383","article-title":"SQuAD: 100000 questions for machine comprehension of text","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process.","author":"Rajpurkar","year":"2016"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/11736790_9"},{"key":"ref42","first-page":"785","article-title":"The second PASCAL recognising textual entailment challenge","volume-title":"Proc. 2nd PASCAL Challenges Workshop Recognising Textual Entailment","author":"Bar-Haim","year":"2006"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3115\/1654536.1654538","article-title":"The third PASCAL recognizing textual entailment challenge","volume-title":"Proc. ACL-PASCAL Workshop Textual Entailment Paraphrasing","author":"Giampiccolo","year":"2007"},{"issue":"8","key":"ref44","first-page":"1","article-title":"The fifth PASCAL recognizing textual entailment challenge","volume":"7","author":"Bentivogli","year":"2009","journal-title":"TAC"},{"key":"ref45","first-page":"1298","article-title":"PAWS: Paraphrase adversaries from word scrambling","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Zhang","year":"2019"},{"key":"ref46","first-page":"9","article-title":"Automatically constructing a corpus of sentential paraphrases","volume-title":"Proc. Workshop Paraphrasing","author":"Dolan","year":"2005"},{"key":"ref47","first-page":"1415","article-title":"Predicting the type and target of offensive posts in social media","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Zampieri","year":"2019"},{"key":"ref48","first-page":"11","article-title":"Hate speech dataset from a white supremacy forum","volume-title":"Proc. Workshop Abusive Lang. Online, Conf. Empirical Methods Natural Lang. Process.","author":"Gibert","year":"2018"},{"key":"ref49","first-page":"809","article-title":"FEVER: A large-scale dataset for fact extraction and verification","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics","author":"Thorne","year":"2018"},{"issue":"5","key":"ref50","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1016\/j.jbi.2012.04.008","article-title":"Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports","volume":"45","author":"Gurulingappa","year":"2012","journal-title":"J. Biomed. Inform."},{"key":"ref51","first-page":"67","article-title":"Learning a better initialization for soft prompts via meta-learning","volume-title":"Proc. Int. Joint Conf. Natural Lang. Process. 3rd Conf. Asia-Pacific Chapter Assoc. Comput. Linguistics","author":"Huang","year":"2023"},{"key":"ref52","first-page":"3251","article-title":"MetaPrompting: Learning to learn better prompts","volume-title":"Proc. Int. Conf. Comput. Linguistics","author":"Hou","year":"2022"},{"key":"ref53","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023"}],"container-title":["IEEE\/ACM Transactions on Audio, Speech, and Language Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6570655\/10304349\/10551912.pdf?arnumber=10551912","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T05:12:28Z","timestamp":1725685948000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10551912\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/taslp.2024.3407519","relation":{},"ISSN":["2329-9290","2329-9304"],"issn-type":[{"value":"2329-9290","type":"print"},{"value":"2329-9304","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}