{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T03:21:47Z","timestamp":1777519307741,"version":"3.51.4"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI.<\/jats:p>","DOI":"10.1007\/s44163-024-00129-0","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T22:01:38Z","timestamp":1716328898000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["LLM potentiality and awareness: a position paper from the perspective of trustworthy and responsible AI modeling"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1740-5517","authenticated-orcid":false,"given":"Iqbal H.","family":"Sarker","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"129_CR1","unstructured":"Zhao WX, Zhou K, Li J, et\u00a0al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023."},{"key":"129_CR2","unstructured":"Huang L et\u00a0al. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232, 2023."},{"key":"129_CR3","doi-asserted-by":"crossref","unstructured":"Sarker IH. AI-driven cybersecurity and threat intelligence: cyber automation, intelligent decision-making and explainability. Springer, 2024.","DOI":"10.1007\/978-3-031-54497-2"},{"key":"129_CR4","doi-asserted-by":"crossref","unstructured":"Sarker IH. Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Comput Sci. 2021; 2(5):377.","DOI":"10.1007\/s42979-021-00765-8"},{"key":"129_CR5","doi-asserted-by":"crossref","unstructured":"Li Y et\u00a0al. Large language models in finance: a survey. In Proceedings ACM Int. Conf. on AI in Finance, 2023.","DOI":"10.1145\/3604237.3626869"},{"key":"129_CR6","doi-asserted-by":"crossref","unstructured":"He K et\u00a0al. A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics. arXiv preprint arXiv:2310.05694, 2023.","DOI":"10.2139\/ssrn.4809363"},{"key":"129_CR7","unstructured":"Gallegos IO et\u00a0al. Bias and fairness in large language models: a survey. arXiv preprint arXiv:2309.00770, 2023."},{"key":"129_CR8","unstructured":"Waleed K, Noorhan A. Fighting lies with intelligence: using large language models and chain of thoughts technique to combat fake news. In Innovative Techniques and Applications of AI: Springer; 2023."},{"issue":"11","key":"129_CR9","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/MC.2023.3305206","volume":"56","author":"M Jovanovic","year":"2023","unstructured":"Mladjan J, Mark C. Connecting ai: Merging large language models and knowledge graph. Computer. 2023;56(11):103\u20138.","journal-title":"Computer"},{"key":"129_CR10","unstructured":"Han J et\u00a0al. Data mining concepts and techniques third edition. 2012."},{"key":"129_CR11","unstructured":"Motlagh F et\u00a0al. Large language models in cybersecurity: state-of-the-art. arXiv preprint arXiv:2402.00891, 2024."},{"issue":"4","key":"129_CR12","doi-asserted-by":"publisher","first-page":"615","DOI":"10.3390\/jcp1040031","volume":"1","author":"K Ameri","year":"2021","unstructured":"Kimia A, Michael H, Hamid S, Juan L, Kalyan P. Cybert: cybersecurity claim classification by fine-tuning the bert language model. J Cybersecur Privacy. 2021;1(4):615\u201337.","journal-title":"J Cybersecur Privacy"},{"key":"129_CR13","doi-asserted-by":"crossref","unstructured":"Dignum V. Responsible artificial intelligence: how to develop and use AI in a responsible way, volume 2156. Springer, 2019.","DOI":"10.1007\/978-3-030-30371-6"},{"issue":"6","key":"129_CR14","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1007\/s10796-021-10146-4","volume":"25","author":"C Trocin","year":"2023","unstructured":"Trocin C, Mikalef P, Papamitsiou Z, Conboy K. Responsible ai for digital health: a synthesis and a research agenda. Inf Syst Front. 2023;25(6):2139\u201357.","journal-title":"Inf Syst Front"},{"key":"129_CR15","unstructured":"Yamada M. Optimizing machine translation through prompt engineering: an investigation into chatgpt\u2019s customizability. arXiv preprint arXiv:2308.01391, 2023."}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00129-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-024-00129-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00129-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T22:04:46Z","timestamp":1716329086000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-024-00129-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":15,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["129"],"URL":"https:\/\/doi.org\/10.1007\/s44163-024-00129-0","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,21]]},"assertion":[{"value":"1 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"40"}}