{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:33:06Z","timestamp":1757309586199,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Ant Group Research Fund"},{"name":"Science and Technology Commission of Shanghai Municipality Grant","award":["22511105902"],"award-info":[{"award-number":["22511105902"]}]},{"name":"Shanghai Sailing Program","award":["23YF1409400"],"award-info":[{"award-number":["23YF1409400"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0103"],"award-info":[{"award-number":["2021SHZDZX0103"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614913","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"1556-1565","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Hierarchical Prompt Tuning for Few-Shot Multi-Task Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8671-2302","authenticated-orcid":false,"given":"Jingping","family":"Liu","sequence":"first","affiliation":[{"name":"East China University of Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6267-6389","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9736-0231","authenticated-orcid":false,"given":"Zujie","family":"Liang","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9026-7515","authenticated-orcid":false,"given":"Haiyun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-9591","authenticated-orcid":false,"given":"Yanghua","family":"Xiao","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6928-1685","authenticated-orcid":false,"given":"Feng","family":"Wei","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2685-9247","authenticated-orcid":false,"given":"Yuxi","family":"Qian","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2136-8873","authenticated-orcid":false,"given":"Zhenghong","family":"Hao","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8051-1278","authenticated-orcid":false,"given":"Bing","family":"Han","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Attentional Mixtures of Soft Prompt Tuning for Parameter-efficient Multi-task Knowledge Sharing. ArXiv","author":"Asai Akari","year":"2022","unstructured":"Akari Asai , Mohammadreza Salehi , Matthew E Peters , and Hannaneh Hajishirzi . 2022. Attentional Mixtures of Soft Prompt Tuning for Parameter-efficient Multi-task Knowledge Sharing. ArXiv ( 2022 ), arXiv--2205.11961. Akari Asai, Mohammadreza Salehi, Matthew E Peters, and Hannaneh Hajishirzi. 2022. Attentional Mixtures of Soft Prompt Tuning for Parameter-efficient Multi-task Knowledge Sharing. ArXiv (2022), arXiv--2205.11961."},{"key":"e_1_3_2_1_2_1","volume-title":"Meta-graph: Few shot link prediction via meta learning. arXiv preprint arXiv:1912.09867","author":"Bose Avishek Joey","year":"2019","unstructured":"Avishek Joey Bose , Ankit Jain , Piero Molino , and William L Hamilton . 2019 . Meta-graph: Few shot link prediction via meta learning. arXiv preprint arXiv:1912.09867 (2019). Avishek Joey Bose, Ankit Jain, Piero Molino, and William L Hamilton. 2019. Meta-graph: Few shot link prediction via meta learning. arXiv preprint arXiv:1912.09867 (2019)."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings, Part X 16","author":"Dvornik Nikita","year":"2020","unstructured":"Nikita Dvornik , Cordelia Schmid , and Julien Mairal . 2020 . Selecting relevant features from a multi-domain representation for few-shot classification. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020 , Proceedings, Part X 16 . Springer, 769--786. Nikita Dvornik, Cordelia Schmid, and Julien Mairal. 2020. Selecting relevant features from a multi-domain representation for few-shot classification. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part X 16. Springer, 769--786."},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics. 4952--4964","author":"Fei Zhaoye","year":"2022","unstructured":"Zhaoye Fei , Yu Tian , Yongkang Wu , Xinyu Zhang , Yutao Zhu , Zheng Liu , Jiawen Wu , Dejiang Kong , Ruofei Lai , Zhao Cao , 2022 . Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding . In Proceedings of the 29th International Conference on Computational Linguistics. 4952--4964 . Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, et al. 2022. Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding. In Proceedings of the 29th International Conference on Computational Linguistics. 4952--4964."},{"key":"e_1_3_2_1_5_1","unstructured":"Hang Gao Zheng Shou Alireza Zareian Hanwang Zhang and Shih-Fu Chang. 2018. Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks. In Neural Information Processing Systems.  Hang Gao Zheng Shou Alireza Zareian Hanwang Zhang and Shih-Fu Chang. 2018. Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks. In Neural Information Processing Systems."},{"key":"e_1_3_2_1_6_1","volume-title":"International Conference on Machine Learning. PMLR, 8678--8690","author":"He Yun","year":"2022","unstructured":"Yun He , Steven Zheng , Yi Tay , Jai Gupta , Yu Du , Vamsi Aribandi , Zhe Zhao , YaGuang Li , Zhao Chen , Donald Metzler , 2022 . Hyperprompt: Prompt-based task-conditioning of transformers . In International Conference on Machine Learning. PMLR, 8678--8690 . Yun He, Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, et al. 2022. Hyperprompt: Prompt-based task-conditioning of transformers. In International Conference on Machine Learning. PMLR, 8678--8690."},{"key":"e_1_3_2_1_7_1","volume-title":"International Conference on Machine Learning. PMLR, 2790--2799","author":"Houlsby Neil","year":"2019","unstructured":"Neil Houlsby , Andrei Giurgiu , Stanislaw Jastrzebski , Bruna Morrone , Quentin De Laroussilhe , Andrea Gesmundo , Mona Attariyan , and Sylvain Gelly . 2019 . Parameter-efficient transfer learning for NLP . In International Conference on Machine Learning. PMLR, 2790--2799 . Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning. PMLR, 2790--2799."},{"key":"e_1_3_2_1_8_1","volume-title":"Lora: Low-rank adaptation of large language models. ArXiv","author":"Hu Edward J","year":"2021","unstructured":"Edward J Hu , Yelong Shen , Phillip Wallis , Zeyuan Allen-Zhu , Yuanzhi Li , Shean Wang , Lu Wang , and Weizhu Chen . 2021 . Lora: Low-rank adaptation of large language models. ArXiv (2021), arXiv--2106.09685. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. ArXiv (2021), arXiv--2106.09685."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00147"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1356"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of NAACL-HLT. 4171--4186","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of NAACL-HLT. 4171--4186 . Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT. 4171--4186."},{"key":"e_1_3_2_1_12_1","volume-title":"Adversarial multi-task learning for text classification. arXiv preprint arXiv:1704.05742","author":"Liu Pengfei","year":"2017","unstructured":"Pengfei Liu , Xipeng Qiu , and Xuanjing Huang . 2017. Adversarial multi-task learning for text classification. arXiv preprint arXiv:1704.05742 ( 2017 ). Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2017. Adversarial multi-task learning for text classification. arXiv preprint arXiv:1704.05742 (2017)."},{"key":"e_1_3_2_1_13_1","first-page":"1","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu , Weizhe Yuan , Jinlan Fu , Zhengbao Jiang , Hiroaki Hayashi , and Graham Neubig . 2023 . Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing . Comput. Surveys , Vol. 55 , 9 (2023), 1 -- 35 . Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys, Vol. 55, 9 (2023), 1--35.","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"e_1_3_2_1_15_1","volume-title":"Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts. ArXiv","author":"Liu Xiangyang","year":"2022","unstructured":"Xiangyang Liu , Tianxiang Sun , Xuanjing Huang , and Xipeng Qiu . 2022b. Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts. ArXiv ( 2022 ), arXiv--2210.11292. Xiangyang Liu, Tianxiang Sun, Xuanjing Huang, and Xipeng Qiu. 2022b. Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts. ArXiv (2022), arXiv--2210.11292."},{"key":"e_1_3_2_1_16_1","volume-title":"arXiv e-prints","author":"Liu Xiao","year":"2021","unstructured":"Xiao Liu , Yanan Zheng , Zhengxiao Du , Ming Ding , Yujie Qian , Zhilin Yang , and Jie Tang . 2021. GPT Understands , Too. arXiv e-prints ( 2021 ), arXiv--2103.10385. Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021. GPT Understands, Too. arXiv e-prints (2021), arXiv--2103.10385."},{"key":"e_1_3_2_1_17_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. ArXiv","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu , Myle Ott , Naman Goyal , Jingfei Du , Mandar Joshi , Danqi Chen , Omer Levy , Mike Lewis , Luke Zettlemoyer , and Veselin Stoyanov . 2019 . Roberta: A robustly optimized bert pretraining approach. ArXiv (2019), arXiv--1907.11692. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. ArXiv (2019), arXiv--1907.11692."},{"key":"e_1_3_2_1_18_1","volume-title":"Learning multiple tasks with multilinear relationship networks. Advances in neural information processing systems","author":"Long Mingsheng","year":"2017","unstructured":"Mingsheng Long , Zhangjie Cao , Jianmin Wang , and Philip S Yu. 2017. Learning multiple tasks with multilinear relationship networks. Advances in neural information processing systems , Vol. 30 ( 2017 ). Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Philip S Yu. 2017. Learning multiple tasks with multilinear relationship networks. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.091"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1250"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.626"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"e_1_3_2_1_23_1","volume-title":"A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. arXiv preprint arXiv:2205.06743","author":"Song Yisheng","year":"2022","unstructured":"Yisheng Song , Ting Wang , Subrota K Mondal , and Jyoti Prakash Sahoo . 2022. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. arXiv preprint arXiv:2205.06743 ( 2022 ). Yisheng Song, Ting Wang, Subrota K Mondal, and Jyoti Prakash Sahoo. 2022. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. arXiv preprint arXiv:2205.06743 (2022)."},{"key":"e_1_3_2_1_24_1","volume-title":"A Survey. ArXiv","author":"Vanschoren Joaquin","year":"2018","unstructured":"Joaquin Vanschoren . 2018. Meta-Learning : A Survey. ArXiv , Vol. abs\/ 1810 .03548 ( 2018 ). Joaquin Vanschoren. 2018. Meta-Learning: A Survey. ArXiv, Vol. abs\/1810.03548 (2018)."},{"key":"e_1_3_2_1_25_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5446"},{"key":"e_1_3_2_1_27_1","volume-title":"Towards Unified Prompt Tuning for Few-shot Text Classification. ArXiv","author":"Wang Jianing","year":"2022","unstructured":"Jianing Wang , Chengyu Wang , Fuli Luo , Chuanqi Tan , Minghui Qiu , Fei Yang , Qiuhui Shi , Songfang Huang , and Ming Gao . 2022b. Towards Unified Prompt Tuning for Few-shot Text Classification. ArXiv ( 2022 ), arXiv--2205.05313. Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, and Ming Gao. 2022b. Towards Unified Prompt Tuning for Few-shot Text Classification. ArXiv (2022), arXiv--2205.05313."},{"key":"e_1_3_2_1_28_1","volume-title":"S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning. ArXiv","author":"Wang Yabin","year":"2022","unstructured":"Yabin Wang , Zhiwu Huang , and Xiaopeng Hong . 2022a. S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning. ArXiv ( 2022 ), arXiv--2207.12819. Yabin Wang, Zhiwu Huang, and Xiaopeng Hong. 2022a. S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning. ArXiv (2022), arXiv--2207.12819."},{"key":"e_1_3_2_1_29_1","volume-title":"James Tin-Yau Kwok, and Lionel Ming shuan Ni","author":"Wang Yaqing","year":"2019","unstructured":"Yaqing Wang , Quanming Yao , James Tin-Yau Kwok, and Lionel Ming shuan Ni . 2019 . Generalizing from a Few Examples : A Survey on Few-Shot Learning. arXiv: Learning ( 2019). Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, and Lionel Ming shuan Ni. 2019. Generalizing from a Few Examples: A Survey on Few-Shot Learning. arXiv: Learning (2019)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.403"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2114"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6142"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511921"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070203"},{"key":"e_1_3_2_1_37_1","volume-title":"Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198","author":"Zhong Qihuang","year":"2023","unstructured":"Qihuang Zhong , Liang Ding , Juhua Liu , Bo Du , and Dacheng Tao . 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 ( 2023 ). Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 (2023)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3004555"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Birmingham United Kingdom","acronym":"CIKM '23"},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614913","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614913","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:43Z","timestamp":1750178203000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614913"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":39,"alternative-id":["10.1145\/3583780.3614913","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614913","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}