{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:25:19Z","timestamp":1753885519847,"version":"3.41.2"},"reference-count":2,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Robot. Res."],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p> Large Language Models (LLMs), exemplified by GPT-4, have transcended traditional boundaries in language processing, demonstrating remarkable capabilities in understanding and generating nuanced text. Crucially, these models are pioneering a paradigm shift in Artificial Intelligence (AI) applications \u2014 from solving narrowly defined problems to navigating complex, real-world scenarios. Such a shift is based on a simple and fundamental principle: LLMs can process any data that can be serialized and tokenized, enabling them to engage in multifaceted reasoning and utilize diverse tools. This capability positions LLMs to operate effectively in broader, more intricate contexts, marking a leap in AI\u2019s practical applicability and potential. <\/jats:p>","DOI":"10.1142\/s2972335324010014","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T03:39:35Z","timestamp":1704685175000},"source":"Crossref","is-referenced-by-count":0,"title":["Generative AI for Complex Scenarios: Language Models are Sequence Processors"],"prefix":"10.1142","volume":"01","author":[{"given":"Bowen","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Tsinghua University, Beijing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8758-9484","authenticated-orcid":false,"given":"Ning","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Tsinghua University, Beijing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"volume-title":"North American Chapter of the Association for Computational Linguistics","year":"2019","author":"Devlin J.","key":"S2972335324010014BIB007"},{"key":"S2972335324010014BIB008","first-page":"140:1","volume":"21","author":"Raffel C.","year":"2019","journal-title":"J. Mach. Learn. Res."}],"container-title":["International Journal of Artificial Intelligence and Robotics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S2972335324010014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T06:18:28Z","timestamp":1709533108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S2972335324010014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":2,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1142\/S2972335324010014"],"URL":"https:\/\/doi.org\/10.1142\/s2972335324010014","relation":{},"ISSN":["2972-3353","2972-3361"],"issn-type":[{"type":"print","value":"2972-3353"},{"type":"electronic","value":"2972-3361"}],"subject":[],"published":{"date-parts":[[2024,3]]},"article-number":"2401001"}}