# Not Minds, but Signs: Reframing LLMs through Semiotics

arXiv: https://arxiv.org/abs/2505.17080 · HTML: https://mlsanity.com/paper/2505.17080/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Predictability and Surprise in Large Generative Models](https://mlsanity.com/paper/2202.07785/) (2022)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [Meaning without reference in large language models](https://mlsanity.com/paper/2208.02957/) (2022)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023)
- [Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges](https://mlsanity.com/paper/2409.02387/) (2024)
- [Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models](https://mlsanity.com/paper/2502.07077/) (2025)
- [Does ChatGPT Have a Mind?](https://mlsanity.com/paper/2407.11015/) (2024)
- [Thinking beyond the anthropomorphic paradigm benefits LLM research](https://mlsanity.com/paper/2502.09192/) (2025)
- [Do Large Language Models Mirror Cognitive Language Processing?](https://mlsanity.com/paper/2402.18023/) (2024)
- [The brain is a computer is a brain: neuroscience's internal debate and the social significance of the Computational Metaphor](https://mlsanity.com/paper/2107.14042/) (2021)
- [Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses](https://mlsanity.com/paper/2502.15365/) (2025)

## Built on by (most cited first, 2 of 2)

- [The Semiotic Channel Principle: Measuring the Capacity for Meaning in LLM Communication](https://mlsanity.com/paper/2511.19550/) (2025)
- [On Semiotic-Grounded Interpretive Evaluation of Generative Art](https://mlsanity.com/paper/2604.08641/) (2026)

## Abstract (arXiv)

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.