{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:43:20Z","timestamp":1768869800910,"version":"3.49.0"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031702730","type":"print"},{"value":"9783031702747","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\/978-3-031-70274-7_18","type":"book-chapter","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T10:02:08Z","timestamp":1724148128000},"page":"286-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Large Language Model Agent Based Legal Assistant for\u00a0Governance Applications"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2680-0442","authenticated-orcid":false,"given":"Marios Evangelos","family":"Mamalis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4416-8764","authenticated-orcid":false,"given":"Evangelos","family":"Kalampokis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1531-4128","authenticated-orcid":false,"given":"Fotios","family":"Fitsilis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios","family":"Theodorakopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4663-2113","authenticated-orcid":false,"given":"Konstantinos","family":"Tarabanis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"18_CR1","unstructured":"Achiam, J., et al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"18_CR2","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":"18_CR3","unstructured":"Colombo, P., et al.: SaulLM-7B: a pioneering large language model for law. arXiv preprint arXiv:2403.03883 (2024)"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Dahl, M., Magesh, V., Suzgun, M., Ho, D.E.: Large legal fictions: profiling legal hallucinations in large language models. arXiv preprint arXiv:2401.01301 (2024)","DOI":"10.1093\/jla\/laae003"},{"key":"18_CR5","unstructured":"Office of the Federal Register, National Archives and Records Administration: 88 FR 75191 - safe, secure, and trustworthy development and use of artificial intelligence. [government]. Federal Register (2023). https:\/\/www.govinfo.gov\/app\/details\/FR-2023-11-01\/2023-24283"},{"issue":"4","key":"18_CR6","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1080\/13572334.2021.1976947","volume":"27","author":"F Fitsilis","year":"2021","unstructured":"Fitsilis, F.: Artificial Intelligence (AI) in parliaments - preliminary analysis of the Eduskunta experiment. J. Legislative Stud. 27(4), 621\u2013633 (2021). https:\/\/doi.org\/10.1080\/13572334.2021.1976947","journal-title":"J. Legislative Stud."},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Fitsilis, F., Theodorakopoulos, G.: Better regulation and its evolution in the Hellenic legislative and parliamentary system. Statute Law Rev. 45(1), hmae003 (2024)","DOI":"10.1093\/slr\/hmae003"},{"key":"18_CR8","unstructured":"Green, S., Hurst, L., Nangle, B., Cunningham, P., Somers, F., Evans, R.: Software agents: a review. Technical report TCS-CS-1997-06, Department of Computer Science, Trinity College Dublin (1997)"},{"key":"18_CR9","unstructured":"Gruske, C.: Alberta courts caution against using unverified citations generated by AI or large language models (2023). https:\/\/www.canadianlawyermag.com\/practice-areas\/litigation\/alberta-courts-caution-against-using-unverified-citations-generated-by-ai-or-large-language-models\/380383"},{"key":"18_CR10","unstructured":"Harris, M., Wilson, A.: Representative bodies in the AI era: insights for legislatures. https:\/\/www.popvox.org\/ai-vol1"},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Helberger, N., Diakopoulos, N.: ChatGPT and the AI act. Internet Policy Rev. 12(1) (2023). https:\/\/doi.org\/10.14763\/2023.1.1682","DOI":"10.14763\/2023.1.1682"},{"key":"18_CR12","unstructured":"Jiang, A.Q., et al.: Mistral 7B. arXiv preprint arXiv:2310.06825 (2023)"},{"key":"18_CR13","unstructured":"Jiang, A.Q., et al.: Mixtral of experts. arXiv preprint arXiv:2401.04088 (2024)"},{"issue":"1","key":"18_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3580603","volume":"4","author":"E Kalampokis","year":"2023","unstructured":"Kalampokis, E., Karacapilidis, N., Tsakalidis, D., Tarabanis, K.: Understanding the use of emerging technologies in the public sector: a review of horizon 2020 projects. Digit. Gov. Res. Pract. 4(1), 1\u201328 (2023)","journal-title":"Digit. Gov. Res. Pract."},{"key":"18_CR15","unstructured":"Ke, Y.H., et al.: Enhancing diagnostic accuracy through multi-agent conversations: using large language models to mitigate cognitive bias. arXiv preprint arXiv:2401.14589 (2024)"},{"key":"18_CR16","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. In: Advances in Neural Information Processing Systems, vol. 35, pp. 22199\u201322213 (2022)"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Lai, J., Gan, W., Wu, J., Qi, Z., Yu, P.S.: Large language models in law: a survey. arXiv preprint arXiv:2312.03718 (2023)","DOI":"10.1016\/j.aiopen.2024.09.002"},{"key":"18_CR18","doi-asserted-by":"publisher","first-page":"107327482312098","DOI":"10.1177\/10732748231209892","volume":"30","author":"A Laios","year":"2023","unstructured":"Laios, A., et al.: RoBERTa-assisted outcome prediction in ovarian cancer cytoreductive surgery using operative notes. Cancer Control 30, 10732748231209892 (2023)","journal-title":"Cancer Control"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Laios, A., Theophilou, G., De\u00a0Jong, D., Kalampokis, E.: The future of AI in ovarian cancer research: the large language models perspective (2023)","DOI":"10.1177\/10732748231197915"},{"key":"18_CR20","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Advances in Neural Information Processing Systems, vol. 33, pp. 9459\u20139474 (2020)"},{"issue":"6","key":"18_CR21","first-page":"1558","volume":"58","author":"G Listorti","year":"2020","unstructured":"Listorti, G., Basyte-Ferrari, E., Acs, S., Smits, P.: Towards an evidence-based and integrated policy cycle in the EU: a review of the debate on the better regulation agenda. JCMS: J. Common Market Stud. 58(6), 1558\u20131577 (2020)","journal-title":"JCMS: J. Common Market Stud."},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"von Lucke, J., Fitsilis, F., Etscheid, J.: Research and development agenda for the use of AI in parliaments. In: Proceedings of the 24th Annual International Conference on Digital Government Research, pp. 423\u2013433 (2023)","DOI":"10.1145\/3598469.3598517"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Mamalis, M.E., Kalampokis, E., Karamanou, A., Brimos, P., Tarabanis, K.: Can large language models revolutionalize open government data portals? A case of using ChatGPT in statistics.gov.scot. In: Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatics, pp. 53\u201359 (2023)","DOI":"10.1145\/3635059.3635068"},{"key":"18_CR24","unstructured":"Cabinet Office and Central Digital and Data Office: Generative AI framework for HM government (2024). https:\/\/www.gov.uk\/government\/publications\/generative-ai-framework-for-hmg"},{"key":"18_CR25","unstructured":"Cabinet Office\u2019s Central Digital and Data Office, Department for Science, Innovation & Technology (2024). https:\/\/www.nao.org.uk\/reports\/use-of-artificial-intelligence-in-government\/"},{"key":"18_CR26","unstructured":"Petroni, F., et al.: Language models as knowledge bases? arXiv preprint arXiv:1909.01066 (2019)"},{"issue":"8","key":"18_CR27","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"18_CR28","unstructured":"Solove, D.J., Schwartz, P.M.: EU Data Protection and the GDPR. Aspen Publishing (2023)"},{"key":"18_CR29","unstructured":"Tonmoy, S., et al.: A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv:2401.01313 (2024)"},{"key":"18_CR30","unstructured":"Touvron, H., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"18_CR31","unstructured":"Touvron, H., et al.: LLaMA 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"issue":"2","key":"18_CR32","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1177\/0894439320980449","volume":"40","author":"C Van Noordt","year":"2022","unstructured":"Van Noordt, C., Misuraca, G.: Exploratory insights on artificial intelligence for government in Europe. Soc. Sci. Comput. Rev. 40(2), 426\u2013444 (2022)","journal-title":"Soc. Sci. Comput. Rev."},{"key":"18_CR33","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"9","key":"18_CR34","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1038\/s41562-023-01659-w","volume":"7","author":"T Webb","year":"2023","unstructured":"Webb, T., Holyoak, K.J., Lu, H.: Emergent analogical reasoning in large language models. Nat. Hum. Behav. 7(9), 1526\u20131541 (2023)","journal-title":"Nat. Hum. Behav."},{"key":"18_CR35","unstructured":"Wei, J., et al.: Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)"},{"key":"18_CR36","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems, vol. 35, pp. 24824\u201324837 (2022)"},{"issue":"2","key":"18_CR37","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1017\/S0269888900008122","volume":"10","author":"M Wooldridge","year":"1995","unstructured":"Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115\u2013152 (1995)","journal-title":"Knowl. Eng. Rev."},{"key":"18_CR38","unstructured":"Xi, Z., et al.: The rise and potential of large language model based agents: a survey. arXiv preprint arXiv:2309.07864 (2023)"},{"key":"18_CR39","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.aiopen.2021.06.003","volume":"2","author":"C Xiao","year":"2021","unstructured":"Xiao, C., Hu, X., Liu, Z., Tu, C., Sun, M.: Lawformer: a pre-trained language model for Chinese legal long documents. AI Open 2, 79\u201384 (2021)","journal-title":"AI Open"},{"key":"18_CR40","unstructured":"Yao, S., et al.: Tree of thoughts: deliberate problem solving with large language models. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"18_CR41","unstructured":"Yin, R.K.: Case Study Research: Design and Methods, vol.\u00a05. SAGE (2009)"},{"key":"18_CR42","unstructured":"Zhang, Y., et al.: Siren\u2019s song in the AI ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219 (2023)"},{"key":"18_CR43","unstructured":"Zhou, Y., et al.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022)"}],"container-title":["Lecture Notes in Computer Science","Electronic Government"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70274-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T21:33:57Z","timestamp":1732656837000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70274-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031702730","9783031702747"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70274-7_18","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":"21 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EGOV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Electronic Government","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","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":"3 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"egov2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dgsociety.org\/egov-2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}