Relevance Theory and LLMs

The Metaphor Between Natural and Artificial Communication: Analogies and Open Issues.

Authors

DOI:

https://doi.org/10.54103/2531-5994/31700

Keywords:

relevance, prompting, mindreading, metaphor, implicature

Abstract

According to Relevance Theory, communication is justified by the ability of speakers to mutually activate and recognize intentions and to link them to specific cues or contexts. This is a theoretical model that provides a solution to those phenomena that might potentially undermine communication and comprehension processes: the use of the implicit, so-called figurative language – in this case, the system of metaphors – and, finally, indirect speech acts. From this perspective, Relevance Theory emerges as an effective theoretical model insofar as it traces linguistic-communicative activity back to cognitive processes such as inference and mindreading.

Large Language Models (LLMs) are generative AI linguistic models that understand and generate texts in human language. The activation of LLMs encourages the use of a theoretical model such as Relevance Theory, as can be inferred from the device of prompting.

First and foremost, the activation of LLMs via prompting appears to provide all the elements needed to apply Relevance Theory to LLMs and, consequently, to establish increasingly strong analogies between (natural) human communication and that between AI (which is not cognitively structured!) and human users.

The aim of this paper is to examine whether it is legitimate to speak of artificial mind-reading and whether it is appropriate to restore its natural character by associating it strictly with human users. The analysis of certain ‘case studies’ (the continuum between metaphorical and literal expressions, and the continuum between the implicit and the explicit) may indeed provide some insights into establishing a more precise comparison between artificial communication via LLMs and natural communication. In this regard, it will be useful to examine a specific borderline situation: the role of LLMs in relation to both the translation of dead metaphors from one language to another and the partial handling of new metaphors. The lesser influence exerted by lexicalised elements and the difficulty in finding a broad-spectrum alternative solution highlight certain asymmetries between the two forms of communication, without in any way ruling out the possibility of further investigation.

Downloads

Download data is not yet available.

References

Adornetti, I., & Ferretti, F. (2025). Origins of Meaning. Elsevier. DOI: https://doi.org/10.1016/B978-0-323-95504-1.01177-7

Allott, N., & Textor, M. (2026). Literal and metaphorical meaning: in search of a lost distinction. Inquiry: An Interdisciplinary Journal of Philosophy, 69(2), 290-317. DOI: https://doi.org/10.1080/0020174X.2022.2128867

Barattieri di San Pietro, C., Frau, F., Mangiaterra, V., & Bambini, V. (2023). The pragmatic profile of ChatGPT: Assessing the communicative skills of a conversational agent. Sistemi intelligenti, 2, 379-400. DOI: https://doi.org/10.31234/osf.io/ckghw

Branda, F. (2026). When Artificial Intelligence Shapes the way we Think. Philosophy & Technology, 39(42). https://doi.org/10.1007/s13347-026-01055-y DOI: https://doi.org/10.1007/s13347-026-01055-y

Carston, R. (2010). Metaphor: Ad Hoc Concepts, Literal Meaning and Mental Images. Proceedings of the Aristotelian Society, 110(3), 295-321. DOI: https://doi.org/10.1111/j.1467-9264.2010.00288.x

Chandaria, A. (2024, agosto). Mindreading Models: Do LLMs Have Theory of Mind. The Concept. https://www.theconcept.ai/articles/mindreading-models-the-consequences-of-theory-of-mind-in-llms

De Caro, M., & Giovanola, B. (2025). Intelligenze. Etica e politica dell’IA. Il Mulino.

Eragamreddy, N. (2025). The impact of AI on pragmatic competence. Journal of Teaching English for Specific and Academic Purposes, 169-189. DOI: https://doi.org/10.22190/JTESAP250122015E

Ervas, F., & Gola, E. (2013). Lessico e immaginazione nella traduzione delle metafore. E.C. Rivista dell’Associazione italiana Studi semiotici, 7(17), 91-6.

Ervas, F., & Gola, E. (2016). Che cos’è una metafora. Carocci Editore.

Forceville, C. (2025). Relevance theory as the foundation for an inclusive theory of communication. Multimodal Communication, 15(1), 5-20. DOI: https://doi.org/10.1515/mc-2025-0036

Garello, S. (2024). The Enigma of Metaphor. Philosophy, Pragmatics, Cognitive Science. Springer. DOI: https://doi.org/10.1007/978-3-031-56866-4

Grice, P. (1989). Studies in the Way of Words. Harvard University Press.

Jucker, A. H. (2024). Speech Acts. Discursive, Multimodal, Diachronic. Cambridge University Press. DOI: https://doi.org/10.1017/9781009421461

Kövecses, Z. (2014). Conceptual Metaphor Theory and the Nature of Difficulties in Metaphors Translation. In D. R. Miller & E. Monti (a cura di), Translating Figurative Language, Ceslic.

Kövecses, Z. (2020). Extended Conceptual Metaphor Theory. Cambridge University Press. DOI: https://doi.org/10.1017/9781108859127

Kravchenko, N., Kravets, O., Naumova, Y., Uriadova, V., & Shepelska, I. (2024). Analyzing creative metaphors in advertising: Integrating relevance theory and conceptual blending approaches. Amazonia Investiga, 13(82), 177-185. DOI: https://doi.org/10.34069/AI/2024.82.10.14

Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.

Littlemore, J. (2019). Metaphors in the Mind. Sources of Variation in Embodied Metaphor. Cambridge University Press. DOI: https://doi.org/10.1017/9781108241441

Mangiaterra, V., Barattieri di San Pietro, C., Frau, F., Bambini, V., & Al-Azary, H. (2025). On choosing the vehicles of metaphors without a body: evidence from Large Language Models. In Proceedings of the 2nd Workshop on Analogical Abstraction. Cognition, Perception, and Language (Analogy-Angle II), 37-44. DOI: https://doi.org/10.18653/v1/2025.analogyangle-1.4

Published

2026-08-06

How to Cite

Maione, M. (2026). Relevance Theory and LLMs: The Metaphor Between Natural and Artificial Communication: Analogies and Open Issues. DigitCult - Scientific Journal on Digital Cultures, 11(1), 29–44. https://doi.org/10.54103/2531-5994/31700

Issue

Section

Articles
Received 2026-05-19
Accepted 2026-07-16
Published 2026-08-06