Meaning and understanding in large language models.
Can a machine understand the meanings of natural language? Recent developments in the generative large language models (LLMs) of artificial intelligence have led to the belief that traditional philosophical assumptions about machine understanding of language need to be revised. This article critical...
| Publicado en: | Synthese Vol. 205; no. 1; pp. 1 - 22 |
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| Formato: | Artículo |
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Springer Nature
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=181966740&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 181966740 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Jan2025 vid: 205 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 181966740 10.1007/s11229-024-04878-4 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 966KB tig: atl: Meaning and understanding in large language models. aug: au: Havlík, Vladimír affil: https://ror.org/01hyg6578 Institute of Philosophy of the Czech Academy of Sciences, Prague, Czech Republic https://ror.org/040t43x18 University of West Bohemia, Pilsen, Czech Republic sug: keyword: Artificial intelligence Communication and Culture Linguistics Information and Computing Sciences Artificial Intelligence and Image Processing Grounding Large language models Meaning Philosophy and Religious Studies Philosophy Language ab: Can a machine understand the meanings of natural language? Recent developments in the generative large language models (LLMs) of artificial intelligence have led to the belief that traditional philosophical assumptions about machine understanding of language need to be revised. This article critically evaluates the prevailing tendency to regard machine language performance as mere syntactic manipulation and the imitations of understanding, which is only partial and very shallow, without sufficient grounding in the world. The article analyses the views on possible ways of grounding as a condition for successful understanding in LLMs and offers an alternative way in view of the prevailing belief that the success of understanding depends mainly on the referential grounding. An alternative conception seeks to show that semantic fragmentism offers a viable account of natural language understanding and explains how LLMs ground the meanings of linguistic expressions. Uncovering how meanings are grounded allows us to also explain why LLMs’ ability to understand is possible and so remarkably successful. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2025. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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