{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:01:25Z","timestamp":1743048085635,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981472"},{"type":"electronic","value":"9789819981489"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8148-9_5","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"55-67","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Instance-Aware and\u00a0Semantic-Guided Prompt for\u00a0Few-Shot Learning in\u00a0Large Language Models"],"prefix":"10.1007","author":[{"given":"Jinta","family":"Weng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heyan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"5_CR1","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 1877\u20131901 (2020)"},{"key":"5_CR2","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv e-prints arXiv:1810.04805 (2018)"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Gao, T., Fisch, A., Chen, D.: Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723 (2020)","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Han, X., Zhao, W., Ding, N., Liu, Z., Sun, M.: PTR: prompt tuning with rules for text classification. arXiv preprint arXiv:2105.11259 (2021)","DOI":"10.1016\/j.aiopen.2022.11.003"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Hu, S., et al.: Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification. arXiv preprint arXiv:2108.02035 (2021)","DOI":"10.18653\/v1\/2022.acl-long.158"},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1162\/tacl_a_00324","volume":"8","author":"Z Jiang","year":"2020","unstructured":"Jiang, Z., Xu, F.F., Araki, J., Neubig, G.: How can we know what language models know? Trans. Assoc. Comput. Linguist. 8, 423\u2013438 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Kavumba, P., Takahashi, R., Oda, Y.: Are prompt-based models clueless? In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 2333\u20132352 (2022)","DOI":"10.18653\/v1\/2022.acl-long.166"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Koco\u0144, J., et al.: ChatGPT: jack of all trades, master of none. arXiv preprint arXiv:2302.10724 (2023)","DOI":"10.2139\/ssrn.4372889"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: 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 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Li, X.L., Liang, P.: 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 (vol. 1: Long Papers), pp. 4582\u20134597. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.353. https:\/\/aclanthology.org\/2021.acl-long.353","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Lin, Y., Tan, Y.C., Frank, R.: Open sesame: getting inside BERT\u2019s linguistic knowledge (2019)","DOI":"10.18653\/v1\/W19-4825"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Liu, X., Ji, K., Fu, Y., Du, Z., Yang, Z., Tang, J.: P-Tuning v2: prompt tuning can be comparable to fine-tuning universally across scales and tasks (2021)","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"5_CR13","unstructured":"Liu, X., et al.: GPT understands, too. arXiv preprint arXiv:2103.10385 (2021)"},{"key":"5_CR14","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. ArXiv abs\/1907.11692 (2019)"},{"key":"5_CR15","unstructured":"Mahabadi, R.K., et al.: Perfect: prompt-free and efficient few-shot learning with language models. arXiv preprint arXiv:2204.01172 (2022)"},{"key":"5_CR16","unstructured":"OpenAI: Gpt-4 technical report (2023)"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., Huang, X.: Pre-trained models for natural language processing: a survey. Sci. Chin. Technol. Sci. 63(10), 1872\u20131897 (2020). https:\/\/doi.org\/10.1007\/s11431-020-1647-3","DOI":"10.1007\/s11431-020-1647-3"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383\u20132392 (2016)","DOI":"10.18653\/v1\/D16-1264"},{"key":"5_CR19","doi-asserted-by":"publisher","unstructured":"Schick, T., Sch\u00fctze, H.: Exploiting cloze-questions for few-shot text classification and natural language inference. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 255\u2013269. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.20. https:\/\/aclanthology.org\/2021.eacl-main.20","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: AutoPrompt: eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"5_CR21","unstructured":"Sumers, T., Hawkins, R., Ho, M.K., Griffiths, T., Hadfield-Menell, D.: How to talk so AI will learn: instructions, descriptions, and autonomy. In: Advances in Neural Information Processing Systems, vol. 35, pp. 34762\u201334775 (2022)"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Voorhees, E.M., Tice, D.M.: Building a question answering test collection. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 200\u2013207 (2000)","DOI":"10.1145\/345508.345577"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 353\u2013355 (2018)","DOI":"10.18653\/v1\/W18-5446"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Towards unified prompt tuning for few-shot text classification. arXiv preprint arXiv:2205.05313 (2022)","DOI":"10.18653\/v1\/2022.findings-emnlp.37"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Learning to prompt for continual learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 139\u2013149 (2022)","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Webson, A., Pavlick, E.: Do prompt-based models really understand the meaning of their prompts? arxiv abs\/2109.01247 (2021)","DOI":"10.18653\/v1\/2022.naacl-main.167"},{"key":"5_CR27","unstructured":"Wei, J., et al.: Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)"},{"key":"5_CR28","unstructured":"Wei, J., et al.: Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903 (2022)"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Zhong, W., et al.: Improving task generalization via unified schema prompt. arXiv preprint arXiv:2208.03229 (2022)","DOI":"10.1016\/j.aiopen.2023.08.011"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8148-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:33:52Z","timestamp":1710268432000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8148-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981472","9789819981489"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8148-9_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}