{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:04:53Z","timestamp":1781885093301,"version":"3.54.5"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Visual. Comput. Graphics"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tvcg.2022.3209479","type":"journal-article","created":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T20:19:56Z","timestamp":1664828396000},"page":"1-11","source":"Crossref","is-referenced-by-count":144,"title":["Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8995-1683","authenticated-orcid":false,"given":"Hendrik","family":"Strobelt","sequence":"first","affiliation":[{"name":"IBM Research, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Webson","sequence":"additional","affiliation":[{"name":"Brown University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor","family":"Sanh","sequence":"additional","affiliation":[{"name":"Huggingface, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Hoover","sequence":"additional","affiliation":[{"name":"IBM Research, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johanna","family":"Beyer","sequence":"additional","affiliation":[{"name":"Harvard SEAS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanspeter","family":"Pfister","sequence":"additional","affiliation":[{"name":"Harvard SEAS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander M.","family":"Rush","sequence":"additional","affiliation":[{"name":"Huggingface, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-3007"},{"key":"ref38","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.185"},{"key":"ref31","author":"sanh","year":"2021","journal-title":"Multitask prompted training enables zero-shot task generalization"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1487"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1812.08032"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.15"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-demo.12"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744158"},{"key":"ref10","year":"0","journal-title":"Github copilot· your ai pair programmer"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-demo.16"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-demos.22"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3114683"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6319"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1082"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.208"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-demo.21"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-demo.21"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-demo.26"},{"key":"ref28","article-title":"Natural language processing meets journalism","author":"popescu","year":"2017","journal-title":"Proceedings of the 2017 EMNLP Workshop"},{"key":"ref4","article-title":"Evaluating large language models trained on code","author":"chen","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref27","article-title":"True few-shot learning with language models","author":"perez","year":"0","journal-title":"NeurIPS"},{"key":"ref3","article-title":"Language models are few-shot learners","author":"brown","year":"0","journal-title":"Advances in Neural Information Processing Systems 33 Annual Conference on Neural Information Processing Systems 2020 NeurIPS 2020 December 6–12 2020 virtual"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.3028976"},{"key":"ref29","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"radford","year":"2019","journal-title":"OpenAIRE blog"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/11736790_9"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"ref7","first-page":"81","article-title":"The finsim 2020 shared task: Learning semantic representations for the financial domain","author":"el maarouf","year":"0","journal-title":"Proceedings of the Second Workshop on Financial Technology and Natural Language Processing"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-demo.9"},{"key":"ref1","author":"alammar","year":"2020","journal-title":"Interfaces for explaining transformer language models"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-3019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"ref46","author":"zhidkov","year":"0","journal-title":"Advanced GPT-3 playground"},{"key":"ref45","article-title":"Calibrate before use: Improving few-shot performance of language models","volume":"abs 2102 9690","author":"zhao","year":"2021","journal-title":"CoRR"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2865230"},{"key":"ref21","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"abs 2107 13586","author":"liu","year":"2021","journal-title":"CoRR"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/VAST.2017.8585721"},{"key":"ref42","article-title":"Finetuned language models are zero-shot learners","volume":"abs 2109 1652","author":"wei","year":"2021","journal-title":"CoRR"},{"key":"ref23","article-title":"Building games and apps entirely through natural language using openai's code-davinci model","author":"mayne","year":"2022","journal-title":"Andrew Mayne Blog"},{"key":"ref41","article-title":"Do prompt-based models really understand the meaning of their prompts?","volume":"abs 2109 1247","author":"webson","year":"2021","journal-title":"ArXiv"},{"key":"ref26","article-title":"What have language models learned?","author":"pearce","year":"2021","journal-title":"VISxAI Workshop 2021"},{"key":"ref44","article-title":"Character-level convolutional networks for text classification","author":"zhang","year":"0","journal-title":"NIPS"},{"key":"ref25","year":"0","journal-title":"OpenAI playground"},{"key":"ref43","article-title":"Chain of thought prompting elicits reasoning in large language models","author":"wei","year":"2022","journal-title":"ArXiv Preprint"}],"container-title":["IEEE Transactions on Visualization and Computer Graphics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/2945\/4359476\/09908590.pdf?arnumber=9908590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T15:54:28Z","timestamp":1671465268000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9908590\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/tvcg.2022.3209479","relation":{},"ISSN":["1077-2626","1941-0506","2160-9306"],"issn-type":[{"value":"1077-2626","type":"print"},{"value":"1941-0506","type":"electronic"},{"value":"2160-9306","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}