{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:52:21Z","timestamp":1791960741124,"version":"4.3.4"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031606144","type":"print"},{"value":"9783031606151","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-60615-1_7","type":"book-chapter","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T05:01:49Z","timestamp":1716354109000},"page":"98-117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["LLM Based Multi-agent Generation of\u00a0Semi-structured Documents from\u00a0Semantic Templates in\u00a0the\u00a0Public Administration Domain"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2359-5032","authenticated-orcid":false,"given":"Emanuele","family":"Musumeci","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1549-9500","authenticated-orcid":false,"given":"Michele","family":"Brienza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1199-8358","authenticated-orcid":false,"given":"Vincenzo","family":"Suriani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6606-200X","authenticated-orcid":false,"given":"Daniele","family":"Nardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0339-8651","authenticated-orcid":false,"given":"Domenico Daniele","family":"Bloisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"7_CR1","unstructured":"Bsharat, S.M., Myrzakhan, A., Shen, Z.: Principled instructions are all you need for questioning LLaMA-1\/2, GPT-3.5\/4 (2024)"},{"key":"7_CR2","unstructured":"Chen, J., Lin, H., Han, X., Sun, L.: Benchmarking large language models in retrieval-augmented generation (2023). arXiv preprint arXiv:2309.01431"},{"key":"7_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.clsr.2021.105573","volume":"42","author":"J Cobbe","year":"2021","unstructured":"Cobbe, J., Singh, J.: Artificial intelligence as a service: legal responsibilities, liabilities, and policy challenges. Comput. Law Secur. Rev. 42, 105573 (2021)","journal-title":"Comput. Law Secur. Rev."},{"key":"7_CR4","unstructured":"Hegselmann, S., Buendia, A., Lang, H., Agrawal, M., Jiang, X., Sontag, D.: TabLLM: few-shot classification of tabular data with large language models. In: Ruiz, F., Dy, J., van\u00a0de Meent, J.W. (eds.) In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 206, pp. 5549\u20135581. PMLR (2023). https:\/\/proceedings.mlr.press\/v206\/hegselmann23a.html"},{"key":"7_CR5","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing System, vol.\u00a035, pp. 22199\u201322213. Curran Associates, Inc. (2022)"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Krause, S., Stolzenburg, F.: Commonsense reasoning and explainable artificial intelligence using large language models. In: Nowaczyk, S., et al. (eds.) Artificial Intelligence, ECAI 2023 International Workshops. CCIS, vol. 1947, pp. 302\u2013319. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-50396-2_17","DOI":"10.1007\/978-3-031-50396-2_17"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Li, J., Tang, T., Zhao, W.X., Nie, J.Y., Wen, J.R.: Pretrained language models for text generation: a survey (2022). arXiv preprint arXiv:2201.05273","DOI":"10.24963\/ijcai.2021\/612"},{"key":"7_CR8","unstructured":"Li, Z., Peng, B., He, P., Galley, M., Gao, J., Yan, X.: Guiding large language models via directional stimulus prompting (2023). arXiv preprint arXiv:2302.11520"},{"key":"7_CR9","unstructured":"Peng, R., Liu, K., Yang, P., Yuan, Z., Li, S.: Embedding-based retrieval with LLM for effective agriculture information extracting from unstructured data (2023). arXiv preprint arXiv:2308.03107"},{"issue":"8","key":"7_CR10","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)","journal-title":"OpenAI blog"},{"key":"7_CR11","unstructured":"Rasal, S.: LLM harmony: multi-agent communication for problem solving (2024). arXiv preprint arXiv:2401.01312"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Siciliani, L., Ghizzota, E., Basile, P., Lops, P.: OIE4PA: open information extraction for the public administration. J. Intell. Inf. Syst. 62, 273\u2013294 (2024)","DOI":"10.1007\/s10844-023-00814-z"},{"key":"7_CR13","unstructured":"Tang, X., Zong, Y., Zhao, Y., Cohan, A., Gerstein, M.: Struc-bench: Are large language models really good at generating complex structured data? (2023). arXiv preprint arXiv:2309.08963"},{"key":"7_CR14","unstructured":"Tang, Y., Yang, Y.: MultiHop-RAG: Benchmarking retrieval-augmented generation for multi-hop queries (2024). arXiv preprint arXiv:2401.15391"},{"key":"7_CR15","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 24824\u201324837. Curran Associates, Inc. (2022)"},{"key":"7_CR16","unstructured":"White, J., et al.: A prompt pattern catalog to enhance prompt engineering with ChatGPT (2023). arXiv preprint arXiv:2302.11382"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in HCI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60615-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T05:03:38Z","timestamp":1716354218000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60615-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606144","9783031606151"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60615-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}