To address the issue of “AI flavor” in machine writing, a human-machine collaborative model can be adopted to jointly refine the emotional layers and expressive details of the text. Image generated by AI

Large language models (LLMs) are now used at scale to generate content, appearing ubiquitously in news reports, essays, prose and fiction writing, and even academic papers. Yet AI-generated writing often feels polished but vacuous, exquisite but dull, correct but empty. This produces a peculiar and increasingly familiar reading experience commonly described as “AI flavor.” Though the term appears to capture a subjective response, the impression arises from recurring formal features and linguistic patterns amenable to objective analysis. Examining “AI flavor” from a linguistic perspective can help clarify the current limitations of AI-assisted writing, as well as the specific technical and cultural conditions that produce them. It can also prompt creators to reflect on how they might better exercise their own agency and creativity in the age of AI.

Textual features

Reading depends heavily on intuition and accumulated experience, and readers quickly register subtle departures from the habits and rhythms of natural human language through “linguistic intuition.” Formally, texts with an “AI flavor” display conspicuous features in their diction, syntax, and overall structure. Constrained by algorithmic rules, AI writing tends to favor frequent, neutral vocabulary deemed “safe,” producing conservative, mediocre lexical choices lacking individuality and vitality. To remain within these algorithmic preferences for safe lexical terms, it may even deliberately avoid controversial or personally inflected expression. The result is striking homogeneity at the lexical level: dialect, slang, metaphor, idiosyncratic language, and emotionally charged words are rarely encountered.

At the sentence level, AI-generated prose often exhibits an unnaturally smooth flow. Sentence lengths tend to be overly uniform, without either the crispness of short sentences or the intricacy of long ones, thus lacking the diversity and rhythmic dynamism of human language. Structurally, such writing is often heavily templated, formulaic, and over-formalized, relying on supposedly universal formats that can be applied almost regardless of subject matter. Predictable, monotonous sectioning is paired with stock transitions—“first,” “second,” “in conclusion,” “overall,” or “it is worth noting”—that serve merely as decorative scaffolding for spurious logical coherence. The same tendency appears in conclusions built from hollow formulas such as “thus it can be seen,” “it is not difficult to discern,” or “holds significant importance”—statements that sound correct but add no substantive information.

Some online articles are instantly recognizable as AI-generated because they lean too heavily on prefabricated frameworks, mechanically filling rigid three- or four-part templates. AI pours excessively fluid prose into these structures, oblivious to the fact that real writing often involves a struggle with words, wherein authentic expression often betrays the resistance and hesitation inherent in deep thought. Because AI writing deliberately avoids genuine emotional conflict, individual experience, and risky forms of expression, it hovers above lived reality and fails to engage with the complexity, ambiguity, or subtlety of human experience.

Nor does it always know when to stop. Even after the central argument has been articulated, AI-generated writing often continues by restating earlier points, adding redundant summaries, and laboriously emphasizing the “significance” of otherwise pedestrian observations to the considerable annoyance of readers.

Technical roots

The emergence of “AI flavor” stems from the technical limitations of current LLMs in semantic understanding. The generative mechanism of AI-assisted writing can be traced to the fundamentally probabilistic nature of these models. Rather than proceeding from genuine semantic comprehension, an LLM learns statistical patterns from enormous corpora and predicts which token is most likely to come next. Its output therefore tends toward the statistically most probable sequence of words, rather than the most cognitively insightful or intellectually revealing expression. Because LLMs possess no self-awareness and cannot truly understand the meaning of what they say, their output does not arise from genuine insight. Instead, it consists largely of formulations that have a high statistical probability of sounding correct.

The specific corpora and optimization algorithms used to train LLMs are commercial secrets. Nevertheless, their underlying generative logic suggests that mainstream models tend to rely on broadly convergent strategies for producing text. In general terms, a prompt guides the model’s activation and recombination of patterns learned from its training corpus, while reinforcement learning and other optimization techniques further shape the response. Various constraints are then applied to ensure that the output conforms to grammatical rules and other requirements, with sensitive or erroneous material automatically revised, rewritten, or filtered. In systems equipped with retrieval capabilities, relevant information may also be drawn from external knowledge bases before being incorporated into the final response.

Under present technological conditions, many of the algorithms involved in text generation inevitably contribute to “AI flavor.” Reinforcement learning from human feedback (RLHF), for example, is an alignment technique that preferentially rewards safe and compliant responses while discouraging controversial, risky, or highly individualized expression. This can create an invisible “safety straitjacket” during the alignment process. To prevent the generation of biased or potentially harmful material, alignment techniques further smooth away the sharper edges of language, making the resulting expression increasingly cautious and conservative. Models consequently favor frequent, neutral, and positive vocabulary while avoiding dialect, slang, and other strongly personalized forms of expression.

In argumentative writing, this produces texts that may appear comprehensive yet remain superficial across all aspects, lacking truly incisive perspectives or critical analysis. In literary writing, models may similarly avoid rhetorically uncertain or difficult-to-classify devices such as metaphor, irony, and paradox. They may also eschew emotionally charged language because it carries a greater risk of being interpreted as biased, offensive, or otherwise unsafe. In human society, this manner of expression is often disparaged as “bureaucratic jargon,” “empty platitudes,” or “stereotyped writing.”

Potential pathways to mitigate ‘AI flavor’

As long as AI lacks self-awareness, “AI flavor” cannot be eliminated entirely. Nevertheless, it can be mitigated to some extent through improved algorithms and more effective forms of collaborative creation between humans and machines. On the technical side, this requires adjustments to training parameters and deployment strategies, a richer and more varied corpus, and stronger support for individualized expression. One might envision future models incorporating parameters for creativity, criticality, and even a certain degree of “adventurousness,” allowing the human qualities of the output to be controlled more deliberately at the algorithmic level.

For example, a generation interface might include a “stylistic risk coefficient,” allowing users to slide along a scale from “safe and compliant” to “incisive and unconventional.” Within controlled boundaries, the AI could be permitted to challenge established consensus or experiment with dialect, slang, and other unconventional forms of expression, where even occasional mistakes might be preferable to an endless stream of lifeless platitudes. More inclusive parameters, higher-quality data, and better-designed training strategies could, under existing conditions, bring AI-generated prose closer to the vitality of living language and reduce the homogenizing tendencies associated with “AI flavor.”

Mitigating “AI flavor,” however, depends not only on technical refinement or intervention by individual creators. It also requires a system of linguistic evaluation that leaves room for trial and error. Much AI-generated content is cautious and mediocre partly because the training data itself has already been heavily filtered by platform rules and content standards. What LLMs learn is therefore not simply how human beings think or express themselves, but how language is safely formulated under conditions of scrutiny and algorithmic recommendation. Genuinely unlocking the expressive potential of AI writing therefore requires greater diversity within the corpus ecosystem. Training materials could incorporate nonstandard vocabulary and grammar drawn from dialects, jargon, individualized forms of expression rich in emotional tension and intellectual sharpness, exploratory and experimental writing, and narratives from marginalized groups. Exposure to such material would help models to understand language not merely as a conduit for information transfer, but as a medium of emotional warmth and individual experience.

Human-AI collaboration offers another means of refining the emotional layers and expressive details of a text. In some cases, “AI flavor” does not arise entirely from technical limitations. It may also result from a user’s failure to articulate creative requirements with sufficient precision, or from excessive dependence on the machine and a corresponding surrender of creative agency. Lacking embodied experience, AI does not yet possess emotion in any meaningful sense. Its attempts to simulate emotional expression through algorithms therefore tend to retain an unmistakably “plastic” quality. The language may imitate feeling, but it often remains mechanical, stereotyped, and superficial, devoid of both the authenticity of bodily experience and the inner impulse of lived emotion. Human creators must consequently take an active role in guiding and calibrating the process. By refining prompts and continuing to revise textual details, they can bring the generated material closer to authentic human expression and context.

The aim of human-AI collaborative writing is to transform “AI flavor” into a more sentiment-rich “human flavor.” AI-generated content should be treated as raw material rather than a finished product. In an era of human-AI collaboration, creators must maintain their capacity to rewrite, reinterpret, and creatively transform machine-generated text. They must draw on their own experience, judgment, and imagination to give the language a soul, ensuring that technology acts as an aid rather than a master. In essence, “AI flavor” is a fundamentally patterned style lacking individuality and creativity. Efforts to mitigate it therefore represent a journey through which human beings rediscover and reaffirm their own creative powers.

Jian Shengyu is a professor from the School of Art and Design at Yangzhou University.