{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:55:34Z","timestamp":1790844934639,"version":"4.1.0"},"reference-count":59,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/jiot.2024.3470210","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T13:37:52Z","timestamp":1728049072000},"page":"1-1","source":"Crossref","is-referenced-by-count":61,"title":["Efficient Prompting for LLM-Based Generative Internet of Things"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1992-4214","authenticated-orcid":false,"given":"Bin","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0220-7956","authenticated-orcid":false,"given":"Burak","family":"Kantarci","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8218-3490","authenticated-orcid":false,"given":"Jiawen","family":"Kang","sequence":"additional","affiliation":[{"name":"Guandong University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-7416","authenticated-orcid":false,"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8972-8094","authenticated-orcid":false,"given":"Mohsen","family":"Guizani","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3336600"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3360289"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3252040"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3106669"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IOTM.001.2300255"},{"key":"ref6","article-title":"IoT in the era of generative AI: Vision and challenges","author":"Wang","year":"2024","journal-title":"arXiv:2401.01923"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3366906"},{"key":"ref8","article-title":"GPT-4 technical report","volume-title":"arXiv:2303.08774","author":"Achiam","year":"2023"},{"key":"ref9","article-title":"Scaling laws for neural language models","author":"Kaplan","year":"2020","journal-title":"arXiv:2001.08361"},{"key":"ref10","article-title":"Mixtral of experts","author":"Jiang","year":"2024","journal-title":"arXiv:2401.04088"},{"key":"ref11","volume-title":"Meta AI Blog: LLaMA 3","year":"2024"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.iotcps.2023.04.003"},{"key":"ref13","article-title":"On protecting the data privacy of large language models (LLMs): A survey","author":"Yan","year":"2024","journal-title":"arXiv:2403.05156"},{"key":"ref14","first-page":"10764","article-title":"PAL: Program-aided language models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gao"},{"key":"ref15","article-title":"Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks","author":"Chen","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref16","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wei"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2024.3396282"},{"key":"ref18","article-title":"ModuLoRA: Finetuning 3-bit LLMs on consumer GPUs by integrating with modular quantizers","author":"Yin","year":"2023","journal-title":"arXiv:2309.16119"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111080"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.398"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.68"},{"key":"ref22","article-title":"TAT-LLM: A Specialized language model for discrete reasoning over tabular and textual data","author":"Zhu","year":"2024","journal-title":"arXiv:2401.13223"},{"key":"ref23","article-title":"Towards better chain-of-thought prompting strategies: A survey","author":"Yu","year":"2023","journal-title":"arXiv:2310.04959"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.914"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3155656"},{"key":"ref26","article-title":"LLMind: Orchestrating AI and IoT with LLMs for complex task execution","author":"Cui","year":"2023","journal-title":"arXiv:2312.09007"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2024.3384013"},{"key":"ref28","article-title":"LoRa: Low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv:2106.09685"},{"key":"ref29","article-title":"LongLoRa: Efficient fine-tuning of long-context large language models","author":"Chen","year":"2023","journal-title":"arXiv:2309.12307"},{"key":"ref30","article-title":"TableLlama: Towards open large generalist models for tables","author":"Zhang","year":"2023","journal-title":"arXiv:2311.09206"},{"key":"ref31","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023","journal-title":"arXiv:2307.09288"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-eacl.83"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591708"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.551"},{"key":"ref35","first-page":"9237","article-title":"StructGPT: A general framework for large language model to reason over structured data","volume-title":"Proc. Conf. Empir. Methods Nat. Lang. Process.","author":"Jiang"},{"key":"ref36","article-title":"Assessing LLMs\u2019 mathematical reasoning in financial document question answering","author":"Srivastava","year":"2024","journal-title":"arXiv:2402.11194"},{"key":"ref37","first-page":"1","article-title":"Binding language models in symbolic languages","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Cheng"},{"key":"ref38","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Brown"},{"key":"ref39","first-page":"1","article-title":"Automatic chain of thought prompting in large language models","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref40","article-title":"Synthetic prompting: Generating chain-of-thought demonstrations for large language models","author":"Shao","year":"2023","journal-title":"arXiv:2302.00618"},{"key":"ref41","first-page":"12113","article-title":"Automatic prompt augmentation and selection with chain-of-thought from Labeled data","volume-title":"Proc. Find. Assoc. Comput. Linguist.","author":"Shum"},{"key":"ref42","article-title":"Self-consistency improves chain of thought reasoning in language models","author":"Wang","year":"2022","journal-title":"arXiv:2203.11171"},{"key":"ref43","first-page":"5942","article-title":"Answering questions by meta-reasoning over multiple chains of thought","volume-title":"Proc. Conf. Empir. Methods Nat. Lang. Process.","author":"Yoran"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.897"},{"key":"ref45","first-page":"68539","article-title":"Toolformer: Language models can teach themselves to use tools","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Schick"},{"key":"ref46","article-title":"A survey of prompt engineering methods in large language models for different NLP tasks","author":"Vatsal","year":"2024","journal-title":"arXiv:2407.12994"},{"key":"ref47","volume-title":"pandas-dev\/pandas: Pandas","year":"2020"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"ref49","first-page":"6588","article-title":"MultiHiertt: Numerical reasoning over multi hierarchical tabular and textual data","volume-title":"Proc. 60th Annu. Meeting Assoc. Comput. Linguist.","author":"Zhao"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1142"},{"key":"ref51","article-title":"Rethinking detection based table structure recognition for visually rich documents","author":"Xiao","year":"2023","journal-title":"arXiv:2312.00699"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/s10032-023-00453-8"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00459"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.167"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.14778\/3659437.3659452"},{"key":"ref56","article-title":"Complexity-based prompting for multi-step reasoning","author":"Fu","year":"2022","journal-title":"arXiv:2210.00720"},{"key":"ref57","first-page":"1","article-title":"Towards understanding chain-of-thought prompting: An empirical study of what matters","volume-title":"Proc. Workshop Math. Empir. Underst. Found. Models","author":"Wang"},{"key":"ref58","article-title":"TabFact: A large-scale dataset for table-based fact verification","author":"Chen","year":"2019","journal-title":"arXiv:1909.02164"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613165"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/6702522\/10705427.pdf?arnumber=10705427","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T01:20:44Z","timestamp":1734744044000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10705427\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/jiot.2024.3470210","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}