Not Minds, but Signs: Reframing LLMs through Semiotics

2025-05-20

AI Summary

This paper addresses the theoretical impasse in understanding large language models (LLMs) by critiquing the dominant anthropomorphic paradigm that frames them as “cognitive systems.” Method: Drawing on an integrated semiotic framework synthesizing Peircean and Saussurean theories, it reconceptualizes LLMs as technical semiotic agents—“mindless yet meaning-disturbing”—whose function resides in dynamic participation in social symbol manipulation, meaning negotiation, and cultural practice. Through theoretical reconstruction, semiotic modeling, and interdisciplinary case studies (literature, philosophy, education, cultural production), the study systematically develops a non-anthropocentric LLM semiotics. Contribution/Results: The resulting framework dissolves the “understanding illusion,” while furnishing actionable conceptual tools and practical pathways for AI ethics, human–AI co-creation, and critical AI literacy.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper challenges the prevailing tendency to frame Large Language Models (LLMs) as cognitive systems, arguing instead for a semiotic perspective that situates these models within the broader dynamics of sign manipulation and meaning-making. Rather than assuming that LLMs understand language or simulate human thought, we propose that their primary function is to recombine, recontextualize, and circulate linguistic forms based on probabilistic associations. By shifting from a cognitivist to a semiotic framework, we avoid anthropomorphism and gain a more precise understanding of how LLMs participate in cultural processes, not by thinking, but by generating texts that invite interpretation. Through theoretical analysis and practical examples, the paper demonstrates how LLMs function as semiotic agents whose outputs can be treated as interpretive acts, open to contextual negotiation and critical reflection. We explore applications in literature, philosophy, education, and cultural production, emphasizing how LLMs can serve as tools for creativity, dialogue, and critical inquiry. The semiotic paradigm foregrounds the situated, contingent, and socially embedded nature of meaning, offering a more rigorous and ethically aware framework for studying and using LLMs. Ultimately, this approach reframes LLMs as technological participants in an ongoing ecology of signs. They do not possess minds, but they alter how we read, write, and make meaning, compelling us to reconsider the foundations of language, interpretation, and the role of artificial systems in the production of knowledge.

Problem

• Challenges framing LLMs as cognitive systems
• Proposes semiotic view of LLM sign manipulation
• Reframes LLMs as cultural meaning-making agents

Innovation

• Semiotic perspective on LLMs as sign manipulators
• LLMs recombine linguistic forms probabilistically
• Semiotic framework avoids anthropomorphism in LLMs