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A Semiotic Perspective on Generative AI and LLMs
  After the #publication of our #guide on large language models (#LLMs), we received several emails asking for further #insights into the #cultural and #linguistic aspects related to them. Therefore, our DoctorV decided to propose the following #essay, which introduces various perspectives and original theories on the evolution of language, that passes through generative AI and, specifically, LLMs. Enjoy, and please let us know your point of view!

The Semiotics of Natural Language and Generative AI: Reflections on the Evolution of Human Thought and Society


In this essay, we delve into the intricate relationship between natural language and human thought, exploring how the evolution or involution of language reflects changes in society and cognition. We then examine the intersection of natural language and generative artificial intelligence, focusing on Large Language Models (LLMs) from a semiotics perspective. By developing original and complex theories, we aim to identify compatibility, concordance, and continuity between these domains, ultimately inviting readers to engage in open discussion.


Language, as a system of signs and symbols, serves as a representation of human thought and a means of communication. Semiotics, the study of signs and sign processes, offers valuable insights into the nature of language and its role in shaping human cognition. The evolution or involution of language can be viewed as a reflection of societal and cognitive changes. In this context, we consider the growing prominence of generative artificial intelligence and Large Language Models (LLMs) as a point of contact between natural language and machine intelligence.

The Semiotics of Language Evolution and Involution

Language evolution is characterized by the emergence, transformation, and adaptation of linguistic systems. As society evolves, so do the cognitive processes that govern language use. The development of new concepts, ideas, and technologies necessitates the creation of novel signs and symbols. In contrast, language involution refers to a simplification or reduction in linguistic complexity. Some argue that this trend reflects a decline in intellectual capacity or a loss of cultural richness. Peirce's triadic model of semiotics, encompassing representamen (sign), object, and interpretant, provides a framework for understanding language evolution and involution. The continuous interplay between these three components parallels the dynamic nature of language and thought. As society and cognition evolve, the relationship between signs, objects, and interpretants shifts, leading to changes in the structure and function of language.

Generative AI and Large Language Models: A Semiotic Perspective

Generative artificial intelligence, particularly LLMs, has demonstrated remarkable progress in natural language processing. These models learn from vast amounts of textual data, capturing complex patterns and structures inherent in human language. From a semiotic standpoint, LLMs can be viewed as representamen, encoding the relationships between signs and their corresponding objects and interpretants within the training data. The emergence of LLMs highlights the intersection between natural language and machine intelligence. By generating human-like text, these models not only mimic the syntactic and semantic structures of natural language but also raise questions about the nature of meaning and thought in the context of artificial intelligence. Can we consider LLM-generated text as a reflection of machine cognition, akin to natural language as an expression of human thought? This question invites further inquiry into the semiotic dimensions of LLMs and their implications for our understanding of language and cognition.

The Compatibility, Concordance, and Continuity of Natural Language and Generative AI

To address the compatibility, concordance, and continuity between natural language and generative AI, we propose the following theories:
  1. The Co-Evolution Hypothesis: This theory posits that the development of natural language and generative AI is mutually influential. As AI advances, it shapes our understanding and use of language, while the inherent properties of natural language inform the design and function of AI systems. This co-evolutionary process highlights the deep interconnection between human cognition and machine intelligence.
  2. The Linguistic Symbiosis Theory: This notion suggests that generative AI could catalyze language evolution or involution, depending on the nature of its interactions with human language users. By generating novel linguistic constructs or reinforcing existing patterns, AI systems may contribute to the expansion or simplification of natural language.
  3. The Cognitive Continuum Framework: This perspective considers human cognition and machine intelligence as points along a cognitive continuum, with natural language and LLM-generated text representing expressions of thought at different loci. By examining the similarities and differences between human and machine-generated language, we can explore the cognitive and semiotic implications of this continuum.

Invitation for Open Discussion

In conclusion, this essay has explored the semiotics of natural language and generative AI, focusing on the evolution and involution of language in societal and cognitive changes. By examining the intersection between natural language and LLMs, we have proposed original theories that seek compatibility, concordance, and continuity between these domains. As language, thought, and technology continue to evolve, it is crucial for researchers and practitioners to engage in open, interdisciplinary dialogue. We invite readers to reflect upon the ideas presented in this essay, contribute their perspectives, and participate in discussions that advance our understanding of the complex interplay between natural language, human thought, and artificial intelligence.