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Blogstract of
No choice. On the stylistics of AI-generated texts
by Simon Meier-Vieracker
In public discourse surrounding artificial intelligence (AI), the style of texts generated by applications like ChatGPT is often critically examined. Observations frequently include that they sound „too perfect,“ „eerily smooth,“ or lack „a distinct voice“ or „emotional depth“. These comments point to linguistic features, the analysis of which falls under the field of stylistics. But how precisely can we grasp the „style“ of AI-generated content, and what do these capabilities reveal about the underlying mechanisms of Large Language Models (LLMs)? In this paper, I seek to explore these questions.
Many existing stylistic studies on AI texts employ a reductionist concept of style, often limited to countable linguistic features for authorship detection. Instead, I propose applying sociolinguistic and pragmatic style theories, which define style as a meaningful choice among alternatives – a „socially relevant (meaningful) way of performing an action“. This choice generates additional meaning, as what is said always exists within a ‚horizon‘ of other possibilities that were deliberately not chosen.
An experiment demonstrates the remarkable stylistic abilities of LLMs. Three models (ChatGPT 4o, Claude Sonnet 4, Google Gemini 2.5 Flash) are prompted to rewrite a short narrative text into 13 different styles. These styles ranged from „conversational,“ „formal,“ and „emotive“ to „academic“ and „tabloid“. The models executed the task effortlessly, producing texts that differed significantly from the original but maintained consistent stylistic features within each given style. A stylometric cluster analysis confirmed that texts of the same style – even from different LLMs – grouped closely together. For example, the „conversational“ texts consistently showed discourse markers typical for oral narratives, tag questions, direct listener addresses, and interjections, all indicating a dialogical and emotionally engaged speech situation.
Despite this impressive flexibility and style imitation, LLMs do not make choices in the human, meaning-generating sense. They are mechanistic, probabilistic systems that predict the next word in a sequence based on statistical patterns learned during training. Their generated „meaning“ is a „dumb meaning,“ which is ‚parasitically‘ dependent on a human interpreter. LLMs lack the „horizon of an ‚and so forth‘ of action and experience“ that humans possess in their decisions.
How, then, can the success of LLMs in style imitation be explained? The key lies in their training. LLMs are trained on vast amounts of human texts, which contain not only grammatical patterns but also complex patterns of human stylistic choices and metapragmatic categorizations. Metapragmatic references describe how humans themselves typify and categorize styles, such as when we speak of „formal language“ or „tabloid style“. These everyday style labels and their associated stereotypical descriptions are statistically learned by LLMs. When an LLM is prompted to write in a specific style, it reactivates these learned patterns, effectively reproducing „intelligible textures“ it has learned from human texts.
In summary, LLMs master the core principle of style – expressing the same thing in different ways. However, they do not make human choices. Instead, it is the humans who, through our prompts, have the choice to have them write in diverse and interpretable styles, by leveraging the human stylistic and metapragmatic patterns represented within the models. This highlights the fundamental role of metapragmatics in human language use and its indirect influence on the capabilities of AI-generated texts.
(This blogstract was AI-generated by NotebookLM and post-edited by the author. The prompt included the full maniscript and the journal’s definition of blogstract as „a specific abstract form in which the relevance of the study and the essential contents of the discussion paper are summarised in a generally understandable way and made comprehensible for both interdisciplinary and non-academic discourse“)
In the present paper, Simon Meier-Vieracker (MV) argues for a praxeological definition of linguistic ’style‘ when applied to LLM texts. He conducts an experiment in which various LLMs are prompted to produce the same narrative in different ’styles‘, which works relatively well, according to MV’s qualitative and holistic impression (as well as the three examples shown in the text). I think the paper is making an interesting contribution to a tricky issue. But I still have some open questions, in particular wrt. the empirical evidence shown, but also relating to some of the theoretical points.MV first describes the state of the art of „LLM stylistics“, which mainly applies finegrained linguistic features in the way of stylometric authorship attribution to LLMs. I find this careful characterization of the currently prevalent „reductionist“ (MV’s words, I think correctly) view of LLM style very important, and also that he further points out the additional reductionist binary distinction between „human“ and „AI“ authors which often underlies these endeavors. One of the paper’s main contributions is to point this out clearly and also to disprove the assumptions by showing that (a) one LLM can generate texts in many different ’styles‘, and (b) different LLMs also show distinct styles from each other.MV then proceeds to define style not purely as a characteristic of texts but as the availability of different linguistic choices which must be selected by a speaker. In addition, while style is differentiated from ‚register‘ by being less functional, it does serve important communicative purposes by indexing social differences and/or constructing the context it is used in. Thus, stylistic variants are actively picked by a speaker from their range of alternatives.However, this is a point where I think it becomes difficult to apply a notion like ’style‘, which resides in the cognitive (speaker choice) and/or social (contructing contexts, audience design) domain, to LLMs or their output. This leads to my first open question, which is, whether style is actually a property of texts (written in a certain style) or a cognitive concept, i.e. something in a speaker’s mind in a certain situation. Arguably, only textual properties can be observed in LLM output, while they have no intentions and thus also cannot make choices or construct social contexts. While MV acknowledges this problem, it would be nice to see a bit more how one notion of style can be associated with the other.Relatedly, as human readers we probably ascribe ’style‘ based not only on textual properties but also based on context and our biases. Thus, a disjointed text ’style‘ can either be seen as incoherent or full of mistakes (= not style?), or could be read as a distinctive style used by a particular modern writer. Thus, it is difficult to objectively assess the stylistics of LLMs without a clear characterization of what is meant by a „conversational“ or „formal“ style. I would therefore not necessarily conclude from a small experiment like the one shown here that LLMs are able to write in different styles successfully (success ought to be measured by comparing to human texts? see below). The experimental evidence could be strengthened by adding more detail, like which language the prompt and input text were in, whether the chat was restarted after every iteration, whether only one attempt was made for each prompt variant or more. Fig. 1 is interesting and I’d love to see the length of the original input text visualized in there, too.Finally, one apparent contradiction bothers me in the discussion of „LLM style“, which has not been resolved or commented on in the current article, either: In line 114, SM quotes previous research that shows that „AIs tend to use rare words excessively“ [not sure if the apostrophe is there in the original but I wouldn’t put it]. In contrast, lines 158ff quote another common claim from the literature: that LLM output can be detected by its repetiveness or schematic following of patterns, i.e. that it shows less variation than human authored text. But how can both be true at the same time? More frequent rare words would seem to indicate MORE variation, and the second claim states LESS variation (compared to human texts). I would love the author to comment on this – if it makes sense to see this as an apparent contradiction like I do.In sum, I found that the article adds interesting points to a very timely discussion. The experiment itself also provides some further data, which could be used as a starting point for an empirical look at some of the questions raised in the paper. The clustering approach (Fig. 2) is really valuable – though I wonder if it and the qualitative discussion would have benefited from also comparing human texts written in these „styles“. The theoretical discussion in sections 5 and 6 was very enlightening in its attempt to solve the tension between the social meaning of style and its reflex in textual properties (as applied to these LLM generated texts).Thanks!Minor points:There’s a range of typos or editing issues in the pdf version, some of which I’m listing here just to make editing the final paper (if it appears) easier.81 upon possible the effects and impact of -> upon the possible effects of91 distinct different LLMs -> differentiate distinct LLMs110 compromising -> comprising140 „for authorship“: does this mean a binary human/AI distinction?147 into -> in160 Cesare -> De Cesare186 palpable, intricate are missing Italics192 rephrase?194 reddit -> Reddit228 Italics or quotation marks missing?398 I would remove „most“
Gutachten zu
Meier-Vieracker. No Choice: On the Stylistics of AI-Generated Texts.
von Hannes Bajohr (Berkeley)
Empfehlung: publish
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Style, Folk Forensics, and the Post Artificial Condition
In his essay “No Choice: On the Stylistics of AI-Generated Texts,” Simon Meier-Vieracker renders an important service to those of us in the humanities working on large language models as textual machines. Homing in on the question of style, he intervenes in a discussion that often treats this concept as either undefinable and mysterious or overly simplistic, as the parameterization of frequency, length, or similar metrics. The stakes, however, are broader. That AI-generated text has a style (rather than being devoid of it) and that this style is in some way informative about the mechanism of its generation is an assumption that sits at the junction of expert knowledge and commonplace experience: Being confronted with a text we suspect to be synthetic without being able to fully ascertain its provenance is defining of our moment, which I have called “post-artificial.” (Bajohr 2024b)
One characteristic of that moment is that a receptive paranoia – familiar from certain literary theory (Sedgwick 1997) – is becoming a default mode of our collective reading practice. What is “paranoid” about it is not an excess of incontrovertible evidence the inverse, the fact that such evidence is unavailable. Any text now can now, by some measure of plausibility, be suspected to be synthetic, while there is no way to prove the opposite. This is the post-artificial condition.
Yet readers do not respond by suspending judgment altogether. Instead, they cultivate a heightened sense of awareness that resembles connoisseurship in the art-historical sense (Berenson 1927): a practical expertise, a perceptual “knowing-how” that resists full formalization.[1]
Online spaces such as the Reddit forum r/isthisai make this dynamic legible. Users engage in collective detective work here, so that even under conditions of post-artificial doubt, arguments for or against AI origins are put forward by pointing to specific clues. In visual media, these may include background artifacts or a specific output length typical of certain a model’s constraints. For text, the task is less straightforward, though not without recurring heuristics: the use of the m-dash in ChatGPT, the “not this but that” structure, the bulletpointization of information, or the reliably positioned punchline that “ultimately” shows something to be “intriguing” all can serve as shibboleths marking something as AI-generated or even slop.
Of course, as Shaib et al. (2025, 1) point out, in line with the post-artificial condition, “text can be perceived as ‘slop’ even when not generated by AI, and not all AI-generated text reads as ‘slop’.” The paranoia of folk forensics always risks at sliding into the paranoia of what Garbiele de Seta (2024) calls “algorithmic folklore”: confronted with opaque interfaces, readers infer “felt” explanations (“shadow banning”) that may have little evidentiary basis yet serve explanations for otherwise unexplainable experiences. What is more, formerly diagnostic markers, such as the once-notorious “delving into” (Kobak et al. 2025), fade as models change but also feed back into human writing habits. Folk forensics, AI generation, and nonsynthetic text production are linked so that human writers (at least anecdotally) learn to avoid such features lest they themselves fall under suspicion. The result is a perpetual arms race between evolving models and the readers who attempt to identify them in a constant recalibration of forensic intuition.
This throws us back onto the surface. Without knowledge of the underlying mechanics, it is primarily defined by the apprehension of style. Indeed, style may be the primary category by which readers “gut-check” AI text in folk forensic practice. But style, as an almost auratic concept, is also notoriously hard to operationalize. Already 1962, George Kubler bemoaned its fuzziness that, in his eyes, renders it at best a post-hoc label, not an analytic category: “Style is like a rainbow. […] We can see it only briefly while we pause between the sun and the rain, and it vanishes when we go to the place where we thought we saw it.” (Kubler 2008, 118) All attempts to find parameters taming this fuzziness, as Meier-Vieracker suggests in his literature review, tend to be overly reductive when style is relegated to being a function of surface markers such as word frequency, adverb count, readability scores and so on. [2]
For this reason, he proposes taking a step back and reconceptualize the concept at a deeper level. His suggestion is to understand style not so much as a sum of linguistic markers but, in a sociolinguistic and pragmatic way, as a meaningful choice among alternatives. Style, on this account (sharpened by Luhmann’s notion of meaning as a surplus of possible continuations), is constituted by selection against a horizon of discarded possibilities. But this definition comes with a built-in asymmetry. If style is choice, then choice implies intention, and intention is what the model lacks. The result is a familiar conclusion: whatever appears in synthetic text is always only a simulation of the real thing – in this case, style. [3]
With this, Meier-Vieracker moves head-on into the “grounding” debates that have raged within the philosophy of language regarding LLMs (Mollo and Millière 2025), in which two extreme positions confront each other: The first argues that meaning can only emerge if text is “grounded in communicative intent, any model of the world, or any model of the reader’s state of mind” (Bender et al. 2021; Bender and Koller 2020; Hicks et al. 2024); absent these, LLM output is at best meaningless and at worst deceptive. The second position treats intention as a misleading frame and locates meaning elsewhere: in relations internal to language as a system of signs (Gastaldi 2021; Kockelman 2024; Weatherby 2025); in the pragmatic negotiation of attribution (Coeckelbergh and Gunkel 2023); or, more Luhmannian still, in the receiver’s role within the “double contingency” of communication (Esposito 2022).
Meier-Vieracker appears to sympathize with the first camp. For while he acknowledges that what LLMs produce is not non-style either, his explanation of what makes them such performatively competent stylist implies the notion of a “parasitic” meaning, originally employed by Stevan Harnad (1990). LLMs exploit a specifically human layer of language use, namely metapragmatic typifications and labels that circulate in discourse and can be found in training data, so that prompting can reactivate those socially stabilized mappings between labels and linguistic patterns. On this view, what we get then is at best parasitic style.[4]
This position is entirely plausible. It is a direct result of how Meier-Vieracker readjusts the discourse on style from a pure surface phenomenon to a discussion the depth mechanics that make it possible. But precisely because the move is so programmatic, it is worth asking what other questions it forecloses rather than enables. I can think of at least two answers here.
First and obviously, an intent-based notion that treats synthetic style as parasitic has no way of describing it on its own terms. But it is not obvious to me why this asymmetry must be built into the concept at the outset. Without reverting to the counter-position that machines have intentions (Hayles 2019), can one think of ways to uncouple this dependency? The question, in other words, is not whether LLMs “really” choose, but whether there are systematic, describable regularities that warrant being called “style” without being treated merely as a derivative imitation of human stylistic agency. One solution might be to distinguish between style as an analytic category and style as an operative category.
Analytically, “style” is the name we give to patterns we recognize and evaluate on the surface of an output; Meier-Vieracker has shown how quickly that recognition slides into reductive inventories of markers. Operatively, by contrast, “style” would denote a controllable dimension within the model: a single feature that steers generation in stable, interpretable ways. My point here is that the analytic and the operative concepts need not align.
Indeed, there are reasons to suspect a gap or mismatch between our metapragmatic talk of “style” and the way the model represents whatever corresponds to it. In both vision and language, it is notoriously difficult to “disentangle” a single stylistic feature from other co-varying properties. Any attempts of such disentanglement yield an inherent trade-off between style, fluency, and preserving content. (Jafaritazehjani et al. 2020) There is, in other words, no single dimension, no one “knob” called “style,” that can be retrieved from a model. If stylistic effects are similarly multi-dependent in language models – emerging from interactions among wording, syntax, discourse structure, genre expectations, and decoding – then a prompt that merely demands “a style” (“formal,” “florid,” “ornate,” “emotive”) may target a human label rather than a stable internal variable. In other words, we cannot assume that a stylistic predicate on the surface corresponds directly to a discrete, manipulable category on the model’s side. But latter may be exactly what a “synthetic style” would categorize.
Anecdotal experience with LLM-assisted writing points in the same direction. Stephen Marche (2023) reports that asking for prose “in the style of Raymond Chandler” often yields cliché, whereas more specific constraints (spelling out what “Chandler” is supposed to mean, or triangulating via other authors) produce better results. This suggests that “style,” as a metapragmatic label, may activate coarse cultural associations rather than reliably steering the generative process toward the relevant textual dynamics. It also raises the possibility that better control may come precisely from avoiding “style” as a named category and prompting instead via adjacent pragmatic variables: communicative situation, persona, desired rhetorical effect, or intended audience (Liu and Hayles has experimented with these, forthcoming (Liu and Hayles forthcoming)). If that is plausible, then the theoretical task is not only to decide whether synthetic style is “real” or “parasitic,” but to develop concepts for style on the model’s own terms: how such variation is internally distributed, how it can be operationally constrained, and how that operational reality relates (or fails to relate) to our interpretive attributions at the surface. We still lack a vocabulary for that.
Second, the asymmetry can also be challenged from the opposite direction: not by separating human and machine style more cleanly, but by asking whether style can be conceptualized in a way that remains agnostic with respect to underlying intention and thus applies across human and nonhuman origins. If Meier-Vieracker’s style-as-choice is directly connected to the idea that the surface is not enough and we need to find a motivating depth, we may ask whether this is in itself plausible when, in the post-artificial condition, surface is all we have (Bajohr 2025). The demand for a deeper explanation, treating style primarily as evidence for the question “who (or what) wrote this?,” reproduces, at the level of method, the posture of suspicion that the post-artificial condition renders structurally precarious.
But suspicion is not the only possible response. As I have suggested elsewhere (Bajohr 2024b), the post-artificial condition might allow for another consequence. If the detective game becomes structurally unwinnable – if the absence of decisive evidence is not a temporary inconvenience but a permanent feature of our textual ecologies – then paranoia may give way to a more agnostic mode of reading. In that mode, what matters would not origin, but the text’s inherent qualities, its effects, or its usefulness: what it says or does in a given context, through which institutions it circulates, and how readers are positioned by it. If in such a hypothetical situation intent is no longer the privileged ground of meaning, then a definition of style as “meaningful choice” ceases to function as a boundary-policing device that secures the categorical difference between natural and artificial text. Instead, style would have to be reformulated as a reception-relevant phenomenon alone – as pure surface. It may be hard to imagine how to bracket any assumption as to intent, but is not entirely impossible either.
What Meier-Vieracker’s intervention clarifies, then, is not only that “style” cannot be reduced to a checklist of markers, but that style has become the medium in which the waning default of human authorship is negotiated; it may also be the medium in which it is buried so that any conception of style based on this difference may have to be rethought. Further, in a post-artificial condition defined by pervasive uncertainty about origins, stylistics cannot confine itself to deciding whether stylistic effects are “real” or “parasitic.” It must also explain how style organizes reception under epistemic constraint – how it trains intuitions, fuels folk forensics, reshapes writing habits in anticipation of suspicion; but also how it perhaps gradually reorients readers toward an agnostic economy of judgment when stable boundaries between human and synthetic texts can no longer be reliably drawn from the surface that nevertheless remains all we have. Votum: accept
[1]: That connoisseurship should become a metaphor for the perceptive interaction with machine learning artifacts is no accident: It mirrors the statistical nature of current subsymbolic AI in contradistinction to the logic-symbolic reasoning of older, “GOFAI” approaches. Similar metaphors can be found in pitting the atomistic versus the holistic tradition; aggregate versusGestalt; or deontology versus virtue ethics.
[2]: What is more, the style of “an LLM” is mostly treated as monolithic, entrenching its “uncontroversial thingness” rather than investigating the “material-semiotic specificity” (Suchman 2023) of individual models in clearly circumscribed states.
[3]: If I understand his argument correctly, this point is made via the generativist tradition Meier-Vieracker briefly reconstructs: in Rosengren’s model, “style” can be formalized as a set of probabilistic “stylistic performance rules” that modulate linguistic output and can, in principle, be described without immediately collapsing into a mere marker checklist. Meier-Vieracker’s point, however, seems to be that the analogy to LLMs remains limited: while LLMs also yield distributional regularities, their probabilities are not confined to a separable “stylistic layer” that comes after grammatical derivation. Rather, they are constitutive of generation as such, so that probabilistic profiling may be diagnostically useful yet still falls short of style as “meaningful choice” in a pragmatic sense.
[4]: While Meier-Vieracker cites my notion of “dumb meaning”(Bajohr 2024a) to make his case, I am not tempted to speak of “dumb style” here, partly because the former term was originally suggested as amiddleposition between the camps mentions. The point was precisely that meaning in LLMs maynotbe solelyparasitic.
Literatur
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Bajohr, Hannes. 2024b. “On Artificial and Post-Artificial Texts: Machine Learning and the Reader’s Expectations of Literary and Non-Literary Writing.” Poetics Today 45 (2): 331–61.
Bajohr, Hannes. 2025. “Surface Reading LLMs: Synthetic Text and Its Styles.” arXiv:2510.22162. Preprint, arXiv. https://doi.org/10.48550/arXiv.2510.22162.
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. https://doi.org/10.1145/3442188.3445922.
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Gutachten zu
Meier-Vieracker: No choice. On the stylistics of AI-generated texts
von Katrin Lehnen
Empfehlung: publish
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The article is concerned with questions of AI-specific stylistics. It asks about the similarities and differences between human and AI-generated text styles with the aim of contributing, on a theoretical level, to a stylistics of AI. At the core of the argument lies a praxeological concept of style, developed in dialogue with various interdisciplinary approaches (including Luhmann) and introducing the choice–/no-choice paradigm as a key distinguishing concept. An empirical study illustrates and exemplifies the theoretical considerations.
To me, this is a smart, interesting, and theoretically innovative contribution whose linguistic elegance and lightness of style also made it a pleasure to read. I find the author’s reflections original, well-informed, and highly illuminating for the theoretical debate—not only on the concept of style, and not only for AI-generated texts. What convinces me most is the derivation of a praxeological concept of style that allows for a different, more critical view of previous approaches to style and—this is a personal remark—captures and resolves my own discomfort with debates that focus heavily on the “deficits” of AI output (hallucinations, etc.).
The way in which the argument unfolds throughout the text is consistently persuasive. The review of previous research provides a nuanced view of prevailing assumptions and of the weaknesses associated with a traditional concept of style. It is convincingly shown that one deficit of existing studies lies in their reliance on largely holistic and unspecific prompts, which means that the results of style analyses remain confined to the generic style of the output (e.g., “less involvement”). Although it is self-evident that the quality of a prompt crucially shapes the quality of the output—and thus also the possibilities of constructing a more complex analysis of style and stylistic variation (“The mentioned studies make use of concise and static prompts to retrieve a kind of standard response from used LLMs. However, this approach neglects the fact that LLMs are generally able to produce texts in a variety of styles as observed in the training data during the training process.”)—the author’s discussion nevertheless deserves credit for addressing this gap and proposing a more adequate research design than the often reductionist setups of empirical studies.
The empirical study takes up the theoretical and empirical weaknesses identified in the first part and translates them into a research design that is well suited to eliciting and analysing stylistic variants of AI-generated texts. The data provide an exemplary illustration and deepening of the problems described in the theoretical section. However, the analysis of the results is, in my view, somewhat too coarse (see point 2 below).
The empirical study is very well aligned with the “more dynamic,” interaction-oriented concept of style developed in the theoretical section. It convincingly shows where the “real” differences between AI-generated and human-generated texts lie: The choice/no-choice paradigm for distinguishing between AI and human stylistic production proves to be a decisive conceptual move and is presented very clearly in the overall argument.
For me, the author’s reflections have generated new insights that effectively counter arguments accusing LLMs of deficits, hallucinations, conformity, etc.—arguments in which the technical foundations of generative AI, despite general awareness of probabilistic text generation, are repeatedly lost from view. The distinction between choice and no-choice, so to speak, takes the wind out of the sails of these debates (without creating a lull!). Please excuse this slightly awkward metaphor.
I have two critical comments and one additional remark—all intended as suggestions, not as requirements for revision.
I believe it would be worthwhile to motivate more fundamentally, at the beginning of the paper, why the question of an AI stylistics is relevant and interesting. Although the paper initially grounds the need for an AI stylistics in a general “public debate” about AI content and its stylistic qualities, and identifies one aim as “to show that it is fruitful to apply concepts from sociolinguistic and pragmatic style theories to the analysis of AI-generated texts, as this highlights the similarities but also the differences between human and AI-generated styles” (emphasis K.L.), I do not find this immediately compelling—especially when compared to the stronger arguments developed later (Sections 2, 3). One could object from the outset that AI texts are not “texts” in the strict sense but, following Schneider (2024), “intelligible textures,” which users nevertheless read and interpret as “intelligent texts” (Schneider 2024: 15). What I mean is this: the very fact that we attribute stylistic features to AI texts is, at first glance, surprising. One could argue that the concept of style is closely tied to the concept of text, and that this, in turn, is tied to notions of authorship. (This is admittedly a somewhat pointed argument, since we do not discuss style for all text types (e.g., instruction manuals) but primarily for those where originality and authorship matter). It may therefore be worthwhile to take a step back at the beginning and ask what is implied when we apply the concept of style to machine-generated texts. I know that the paper develops this systematically later, but it was my first stumbling point, and I immediately wondered why it should be relevant to compare AI-generated and human texts from a stylistic perspective.
Elsewhere and later in the text, it is very well explained why style becomes a relevant aspect of professional debates—particularly in educational contexts, where it is important to distinguish between texts produced by learners and texts produced by AI systems (“An important branch of empirical research into the stylistic properties of AI-generated texts stems from a practical need: detecting texts produced by or with the help of generative AI, particularly in the educational domain.”). For the theoretical aims of the article, however, I consider this secondary. The second aim—“to show that examining the ability of LLMs to write in different styles raises interesting theoretical questions about language and style in general”—is, by contrast, immediately compelling.
Although the introduction states that the data serve merely an illustrative function, a few more exemplary analyses of AI outputs would be helpful—especially in light of the fundamental critique of existing studies (“A more detailed reference to the functions and effects of these measurable style qualities is still missing,” ll. 122–123). Since the figures are still missing in the version I received, it is possible that this point is already addressed in the final layout.
As the article shows, current LLMs can easily vary or imitate the style of source texts/prompts. In this context, it seems particularly interesting to me that “style” occupies a prominent position within the functional spectrum of generative AI tools—at least in two ways:
From a praxeological perspective on style, as proposed by the author, this interplay between The article is concerned with questions of AI-specific stylistics. It asks about the similarities and differences between human and AI-generated text styles with the aim of contributing, on a theoretical level, to a stylistics of AI. At the core of the argument lies a praxeological concept of style, developed in dialogue with various interdisciplinary approaches (including Luhmann) and introducing the choice–/no-choice paradigm as a key distinguishing concept. An empirical study illustrates and exemplifies the theoretical considerations.
To me, this is a smart, interesting, and theoretically innovative contribution whose linguistic elegance and lightness of style also made it a pleasure to read. I find the author’s reflections original, well-informed, and highly illuminating for the theoretical debate—not only on the concept of style, and not only for AI-generated texts. What convinces me most is the derivation of a praxeological concept of style that allows for a different, more critical view of previous approaches to style and—this is a personal remark—captures and resolves my own discomfort with debates that focus heavily on the “deficits” of AI output (hallucinations, etc.).
The way in which the argument unfolds throughout the text is consistently persuasive. The review of previous research provides a nuanced view of prevailing assumptions and of the weaknesses associated with a traditional concept of style. It is convincingly shown that one deficit of existing studies lies in their reliance on largely holistic and unspecific prompts, which means that the results of style analyses remain confined to the generic style of the output (e.g., “less involvement”). Although it is self-evident that the quality of a prompt crucially shapes the quality of the output—and thus also the possibilities of constructing a more complex analysis of style and stylistic variation (“The mentioned studies make use of concise and static prompts to retrieve a kind of standard response from used LLMs. However, this approach neglects the fact that LLMs are generally able to produce texts in a variety of styles as observed in the training data during the training process.”)—the author’s discussion nevertheless deserves credit for addressing this gap and proposing a more adequate research design than the often reductionist setups of empirical studies.
The empirical study takes up the theoretical and empirical weaknesses identified in the first part and translates them into a research design that is well suited to eliciting and analysing stylistic variants of AI-generated texts. The data provide an exemplary illustration and deepening of the problems described in the theoretical section. However, the analysis of the results is, in my view, somewhat too coarse (see point 2 below).
The empirical study is very well aligned with the “more dynamic,” interaction-oriented concept of style developed in the theoretical section. It convincingly shows where the “real” differences between AI-generated and human-generated texts lie: The choice/no-choice paradigm for distinguishing between AI and human stylistic production proves to be a decisive conceptual move and is presented very clearly in the overall argument.
For me, the author’s reflections have generated new insights that effectively counter arguments accusing LLMs of deficits, hallucinations, conformity, etc.—arguments in which the technical foundations of generative AI, despite general awareness of probabilistic text generation, are repeatedly lost from view. The distinction between choice and no-choice, so to speak, takes the wind out of the sails of these debates (without creating a lull!). Please excuse this slightly awkward metaphor.
I have two critical comments and one additional remark—all intended as suggestions, not as requirements for revision.
I believe it would be worthwhile to motivate more fundamentally, at the beginning of the paper, why the question of an AI stylistics is relevant and interesting. Although the paper initially grounds the need for an AI stylistics in a general “public debate” about AI content and its stylistic qualities, and identifies one aim as “to show that it is fruitful to apply concepts from sociolinguistic and pragmatic style theories to the analysis of AI-generated texts, as this highlights the similarities but also the differences between human and AI-generated styles” (emphasis K.L.), I do not find this immediately compelling—especially when compared to the stronger arguments developed later (Sections 2, 3). One could object from the outset that AI texts are not “texts” in the strict sense but, following Schneider (2024), “intelligible textures,” which users nevertheless read and interpret as “intelligent texts” (Schneider 2024: 15). What I mean is this: the very fact that we attribute stylistic features to AI texts is, at first glance, surprising. One could argue that the concept of style is closely tied to the concept of text, and that this, in turn, is tied to notions of authorship. (This is admittedly a somewhat pointed argument, since we do not discuss style for all text types (e.g., instruction manuals) but primarily for those where originality and authorship matter). It may therefore be worthwhile to take a step back at the beginning and ask what is implied when we apply the concept of style to machine-generated texts. I know that the paper develops this systematically later, but it was my first stumbling point, and I immediately wondered why it should be relevant to compare AI-generated and human texts from a stylistic perspective.
Elsewhere and later in the text, it is very well explained why style becomes a relevant aspect of professional debates—particularly in educational contexts, where it is important to distinguish between texts produced by learners and texts produced by AI systems (“An important branch of empirical research into the stylistic properties of AI-generated texts stems from a practical need: detecting texts produced by or with the help of generative AI, particularly in the educational domain.”). For the theoretical aims of the article, however, I consider this secondary. The second aim—“to show that examining the ability of LLMs to write in different styles raises interesting theoretical questions about language and style in general”—is, by contrast, immediately compelling.
Although the introduction states that the data serve merely an illustrative function, a few more exemplary analyses of AI outputs would be helpful—especially in light of the fundamental critique of existing studies (“A more detailed reference to the functions and effects of these measurable style qualities is still missing,” ll. 122–123). Since the figures are still missing in the version I received, it is possible that this point is already addressed in the final layout.
As the article shows, current LLMs can easily vary or imitate the style of source texts/prompts. In this context, it seems particularly interesting to me that “style” occupies a prominent position within the functional spectrum of generative AI tools—at least in two ways:
From a praxeological perspective on style, as proposed by the author, this interplay between practices and formats is highly illuminating. It seems worthwhile to keep in mind the dynamics emerging both from the co-activity of prompting (still human) and output (the system), and from the interaction between practices and (new) formats or program functions.
The article should definitely be published.
Note: The following comment has been automatically translated from German, as my English is not quite strong enough for writing detailed academic commentary. and formats is highly illuminating. It seems worthwhile to keep in mind the dynamics emerging both from the co-activity of prompting (still human) and output (the system), and from the interaction between practices and (new) formats or program functions.
The article should definitely be published.
Note: The following comment has been automatically translated from German, as my English is not quite strong enough for writing detailed academic commentary.