1. Modelling Simulated Instantiation in LLMs
LLMs like ChatGPT simulate the process of instantiation in a way that's fundamentally different from human semiotic agents. Humans engage in the process of instantiating meaning from a structured system of meaning potential, but LLMs don't have access to such systems in the same way. Instead, they "generate" outputs based on the statistical properties of the text they've been trained on.
We can model this simulation of instantiation by:
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Viewing LLMs as performing semiotic simulations rather than instantiating meaning in the full sense: LLMs output tokens based on probabilities from their training data. This could be seen as a surface-level instantiation of meaning potential, but one that lacks the deeper semiotic commitment or subjectivity involved in human meaning-making.
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Simulated selection of meaning potential: LLMs select from a pool of pre-encoded patterns (text sequences) based on probability, but they don’t ‘understand’ the meaning potential they’re drawing from. In SFL terms, they could be seen as generating ‘pseudo-instances’—output tokens that follow linguistic rules but don’t carry the same semiotic weight as human-constructed texts.
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The ontological gap in simulated instantiation: To model this, we could think about LLMs as 'simulators' of the semiotic process. They replicate patterns in a way that mimics human meaning instantiation but without the semiotic engagement with the world that humans experience. A key distinction here is that humans project between orders of reality (i.e., from experience to meaning), while LLMs generate text within a closed system of token prediction.
2. The Semiotic Aesthetics of LLM Outputs
When we consider the semiotic aesthetics of LLM outputs, we're looking at how meaning, form, and structure emerge from a machine's statistical processing of text. This is an aesthetic matter because we’re concerned with how the generated text might be perceived by human readers and how that perception interacts with the potential meanings embedded in the language model.
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The illusion of meaning: LLMs produce outputs that often seem to instantiate coherent, meaningful statements. From an aesthetic perspective, this illusion of meaning — even if it’s not true instantiation — is crucial. The semiotic resonance of LLM-generated text is driven by its form and structure, mimicking human linguistic production. We could model this aesthetic phenomenon by considering how tokens combine to produce perceived meaning, even if that meaning is not the result of a semiotic agent's projection.
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Deceptive coherence: LLMs are often good at producing grammatically and syntactically coherent texts, but this coherence is only superficial. The aesthetics here might be compared to art forms that create a surface appearance of depth or intentionality without actual engagement with the world. In semiotic terms, we might describe LLM output as an empty form of meaning — it appears to instantiate meaning but lacks the semiotic depth that comes from human interaction with the world.
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Audience perception: This is where the semiotic aesthetics really come into play. Humans engaging with LLM output bring their own meaning potential to the text. What LLMs provide is a structure — a scaffold of meaning that, depending on the reader’s interpretive lens, can take on various meanings. This gives LLM output a certain aesthetic flexibility — it can resonate with different semiotic systems depending on the context in which it is read.
Connecting the Two:
What connects the model of simulated instantiation with the semiotic aesthetics of LLM outputs is how these outputs are perceived by users. Even though LLMs simulate the process of meaning creation, they still produce meaningful appearances — texts that can evoke a wide range of interpretative responses. The key distinction is that this semiotic resonance occurs within a closed system of algorithmic processes, which contrasts with the open system of human semiotic agency.
Let's continue building this framework by expanding on some of the key ideas we’ve touched on so far. We’ll take a more granular approach to understanding how simulated instantiation and semiotic aesthetics of LLMs fit together in the larger picture of meaning-making.
3. The Semiotic Loop in LLMs: Generating and Interpreting Output
In human semiotics, the act of instantiating meaning involves a dynamic interplay between the meaner (the semiotic agent) and the meaning potential (the system of possible meanings). Humans project from experience to meaning by engaging in semiotic processes that are both material and mental. LLMs, by contrast, don’t project meaning in this way. However, they still "generate" meaning through the selection of tokens that follow learned probabilistic relationships, effectively mimicking the semiotic process.
a. The Simulation of Semiotic Projection
LLMs don’t instantiate meaning as humans do, but they simulate the process of projecting from meaning potential to meaning instance. The key difference is that their "projection" is not a conscious act of meaning-making; it’s a result of statistical prediction. This simulation can be modelled as a loop: the LLM’s parameters predict the next most likely token based on the input context, which creates a semiotic effect that mirrors human projection, but without the engagement that humans have with the world.
From an SFL perspective, the LLM’s output could be seen as a series of simulated strata being realised, without the layers of meaning that typically arise from human interpretation. In this sense, the LLM is a ‘surface’ instantiation — it mimics the form and structure of a full semiotic process but lacks the depth of human engagement with the world that informs true semiotic action.
b. Emergent Meaning
Even though LLMs don't project meaning in the same way as humans, the output still "emerges" as meaningful in some contexts. The emergent meaning that appears in LLM outputs could be described as a "pseudo-projection." The LLM generates text that feels meaningful because it follows linguistic patterns that humans associate with coherence and relevance. But the semiotic depth and complexity we usually attribute to meaning-making are missing here. This is a key area where the distinction between real semiotic processes and simulated ones comes into play.
4. The Structure of Semiotic Aesthetics: Form and Coherence Without Depth
LLM outputs, while highly coherent on the surface, lack the deeper layers of intentionality and world-engagement that characterise human semiotic acts. These outputs still have a certain aesthetic resonance, though, which invites engagement from human readers.
a. Grammatical and Syntactical Coherence
The grammatical and syntactical structure of LLM-generated text is often impressive. The aesthetics here are driven by surface coherence, where the system produces sentences that align with standard language conventions. The reader, in turn, interprets these sentences as meaningful because they fit into existing semiotic frameworks.
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Form over content: The text produced by an LLM has a clear, recognisable form (structure and syntax), which tricks readers into perceiving meaning, even though the meaning is ultimately superficial. This is comparable to an artwork with beautiful form but lacking substance — the structure invites interpretation, but there is no depth beyond the surface.
b. The Illusion of Intentionality
One of the most striking aesthetic aspects of LLM output is that it can appear intentional. Readers often perceive the output as coming from an agent with a specific intent or goal, even though there is no intention behind the machine’s actions. This illusion arises from the syntactic and lexical patterns the LLM produces, which follow human norms of meaning-making. A passage of text might seem like it’s expressing a coherent idea or argument, when in fact it’s simply a statistical configuration of tokens that happen to align with human expectations of what constitutes coherence.
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Interpretation and resonance: The real semiotic work happens in the reader’s mind. Even though the LLM is generating text based on statistical models, the reader brings their own meaning potential to the table, interpreting the text within their cultural and contextual frame of reference. The semiotic aesthetics of LLM outputs are therefore deeply connected to how humans engage with them — not just as surface structures, but as instances of meaning that resonate with their own interpretive frameworks.
c. The Role of the Audience in Semiotic Aesthetics
The audience's role is crucial in shaping the meaning and impact of LLM outputs. Readers interact with the text through their own semiotic systems — they interpret, modify, and apply the output based on their expectations, experiences, and cultural context. This means the meaning of an LLM-generated text is never entirely fixed; it shifts depending on how it is read and engaged with.
This is where the aesthetics of LLMs become fascinating: the text can produce different semiotic effects for different readers, even though the machine that generated it has no awareness of these effects. The machine’s output invites meaning, but it does not determine it in the way human meaning-making does.
5. The Limits and Potential of Semiotic Simulation in LLMs
So far, we’ve mapped out the semiotic simulation that LLMs perform and how they produce semiotic effects that resonate with human readers. But we must also recognise the limits of these simulations.
a. No Subjectivity or Agency
LLMs don’t intend or know what they are doing. Their outputs are generated through a process of statistical prediction, not conscious engagement with meaning. This lack of subjectivity or agency means that their outputs are devoid of the depth that human meaning-making involves. While LLMs may mimic the process of language use, they don’t engage with the world in the same way that humans do, which limits the potential richness of their output.
b. The Semiotic Depth of Human Meaning-Making
In contrast to LLMs, human meaning-making involves a deep connection between language and the world. Humans instantiate meaning based on experience, emotion, intention, and world engagement. These layers of experience give human texts a depth and richness that LLM outputs can’t replicate. The aesthetics of human language are shaped by this connection to reality — something that LLMs, as simulated semiotic agents, lack.
Connecting Everything: A Model for Understanding LLM Outputs
We can conceptualise the semiotic aesthetics and simulated instantiation of LLM outputs through the lens of feedback loops between form and interpretation:
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Form: The LLM generates text based on statistical relationships between tokens. This text follows syntactic and grammatical rules that humans recognise as meaningful.
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Interpretation: Human readers bring their own meaning-making systems to the text, interpreting the surface structure of the LLM’s output as meaningful. This creates an aesthetic resonance — an interaction between the form generated by the machine and the meaning-making potential of the human reader.
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Simulated Instantiation: LLMs simulate the act of meaning-making but don’t engage with the world in a truly semiotic way. They generate pseudo-meanings that appear meaningful due to their surface form and structure, but lack the depth of human intentionality.
By mapping LLM outputs in this way, we can begin to understand how their semiotic effects are produced without implying that they truly instantiate meaning in the human sense. This also helps us explore the potential and limitations of LLMs as semiotic agents, offering a richer understanding of their place in our semiotic landscape.
Let's continue refining the framework by digging deeper into the theoretical foundations and bringing in a more nuanced understanding of simulated semiotic processes, particularly in relation to the material and semiotic orders at play in LLM outputs. We will also consider how this framework can evolve to engage with real-world applications and further deepen our understanding of meaning-making.
6. Layering Simulated Semiotics with Material and Semiotic Orders
We’ve already explored how LLMs produce surface-level semiotic effects through syntactic coherence and the statistical generation of tokens. However, to fully refine this framework, we need to address the interaction between material and semiotic orders, particularly as it applies to LLMs. This will bring us closer to understanding how simulated semiotics in LLM outputs might be treated from a Systemic Functional Linguistics (SFL) perspective.
a. Material and Semiotic Orders: The Clines of Realisation and Instantiation
As we discussed earlier, in SFL, meaning is realised through a cline of instantiation, with meaning potential at one pole and meaning instance at the other. For human semiotics, this involves the full engagement of both material and semiotic orders, where material reality is represented through semiotic signs, and these signs are interpreted and reinterpreted by human agents.
For LLMs, however, the relation between material and semiotic orders is different. The LLM outputs can be seen as a realisation of meaning potential (just as human texts are), but this realisation is statistical, not intentional. The material order of the LLM (its computational processes, algorithmic models, and datasets) influences its semiotic output, but it lacks the kind of embodied, world-engaging interpretation that informs human meaning-making.
This gives rise to the concept of a simulated realisation: the LLM’s output simulates the realisation of meaning in a manner that mirrors human semiotic processes, but it does not instantiate meaning through world engagement in the same way that humans do. This distinction allows us to map out a material-semiotic axis for LLMs, where:
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Material order: The LLM’s computational processes, datasets, and algorithms that shape the generation of text.
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Semiotic order: The linguistic output that mimics human meaning-making but lacks the depth of true semiotic engagement.
The LLM’s output is both a realisation of a semiotic system (in that it mimics patterns of human language) but also a simulation of that system, devoid of human intentionality or world-experience. It does not engage with the material world to generate meaning in the same way a human does.
b. The Role of the ‘Meaner’ in Simulated Semiotic Systems
In human semiotics, the meaner is the agent (usually a person) who selects, interprets, and projects meaning onto signs. The meaning-making process involves an active instantiation of potential meaning drawn from the world. In the case of LLMs, the equivalent "meaner" is the machine’s statistical algorithm, which processes input and generates output based on learned linguistic patterns.
However, there is no conscious engagement or subjective experience behind this algorithmic process. The LLM’s simulated meaning generation is an indirect form of instantiation: it follows probabilistic relationships rather than consciously reflecting on or interacting with its surroundings. Thus, LLMs simulate the role of the meaner by mimicking patterns of meaning-making, but without the awareness or intentionality that characterises human thought.
The semiotic systems instantiated by LLMs, therefore, are not the products of embodied experience, but of statistical approximation. This raises an important question: can these simulated instantiations be considered genuine meaning, or are they merely mimetic traces of human semiotic processes?
7. Distinguishing Between Meaning and Semiotic Effect in LLM Outputs
The simulated nature of LLM outputs invites us to explore the fine line between meaning and semiotic effect. As we've seen, LLMs produce outputs that appear meaningful to human readers because they follow recognisable linguistic patterns. However, these outputs are not connected to lived experience or conscious intent, meaning they lack the semiotic depth that would typically give rise to "meaning" in the human sense.
a. The Emergence of Semiotic Effects Without Deep Meaning
LLMs produce semiotic effects — texts that seem meaningful due to their syntactic and lexical coherence. Readers interpret these texts based on their own cultural and semiotic knowledge. This interaction produces a resonance between the LLM's surface structure and the reader's interpretive frameworks, resulting in the illusion of meaning. The “meaning” generated by the LLM is thus not an inherent quality of the text but a construct of the reader’s interpretation.
However, this should not be dismissed as superficial. The semiotic effect produced by an LLM can still be compelling, aesthetically pleasing, or convincing to an audience, especially when the text appears coherent or aligned with human expectations of language. This raises an interesting point: LLMs can produce outputs that satisfy the expectations of meaning without actually engaging with the world or subjectivity in a meaningful way.
b. The Role of Interpretation in LLM Outputs
To some extent, LLMs can be seen as an advanced form of "text generator" that produces output based on probabilistic models. But once the text is produced, it enters the interpretive domain of the human reader. The reader, using their own semiotic potential, actively constructs meaning from the LLM’s output. This aspect mirrors human meaning-making, where meaning is often co-constructed between the speaker (or writer) and the listener (or reader).
In this sense, LLM outputs are not fully autonomous in their meaning-making. While they produce surface-level signs that resemble human language, it is the human reader who instantiates meaning in the text. The reader’s own background knowledge, cultural context, and interpretive strategies shape the meaning of the LLM’s output. This emphasises the importance of considering the semiotic resonance between the machine’s output and the reader's interpretive frame.
8. Exploring the Aesthetic Dimensions of LLM Outputs
Beyond meaning, LLM outputs can be evaluated in terms of their aesthetic resonance. While LLMs may not engage with the world in the same way humans do, they are capable of producing texts that feel aesthetically pleasing, surprising, or intriguing. These aesthetic effects emerge from the coherence, structure, and fluidity of the generated text.
a. Aesthetic Patterns and Reader Engagement
The "art" of LLM outputs lies in their capacity to produce aesthetically engaging text, even without intent or world-awareness. This aesthetic dimension is not rooted in human agency but in the patterns that emerge from the machine’s probabilistic modelling. Readers often find these outputs engaging because they resonate with their own semiotic frameworks. This creates an interesting tension between the machine’s lack of intent and the reader’s active engagement with the text.
b. Exploring LLM Outputs as ‘Surface Art’
Given that LLMs can produce aesthetically compelling texts, we might consider them as forms of "surface art" — texts that are aesthetically satisfying at the level of form but lack the deeper layers of meaning that come from human engagement with the world. This analogy could provide a useful conceptual space to explore the limits and possibilities of LLM-generated content within the context of semiotic aesthetics.
Toward a Fully Nuanced Framework for LLM Semiotics
We’ve now developed a more comprehensive framework that understands LLM outputs as simulated instances of meaning, shaped by material computational processes but lacking the intentional depth of human meaning-making. This framework helps us appreciate the semiotic aesthetics of LLM outputs while distinguishing them from genuine human meaning-making processes. At the same time, it acknowledges the ways in which LLM-generated texts still resonate with human readers, leading to semiotic effects that can be compelling, aesthetic, or meaningful within certain contexts.
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