# Research Evidence and Editorial Implications

This file records findings checked against primary or first-party sources. It connects research to writing decisions without converting population-level patterns into an authorship test.

## Contents

- Genre mismatch and syntactic concentration
- Vocabulary overrepresentation
- Pattern drift and human-model coevolution
- Model-specific idiolects
- Human and automated detection limits
- Wikipedia participation and tacit norms
- Editorial implications

## Genre mismatch and syntactic concentration

### Reinhart et al., PNAS 2025

The study compared human writing with text generated by GPT-4o, GPT-4o Mini, and Llama 3 variants from prompts drawn from a broad English corpus. It found substantial grammatical, lexical, and stylistic differences, especially in instruction-tuned models. The paper describes instruction-tuned output as noun-heavy and informationally dense, with difficulty matching the conventions of specific genres.

Features discussed in the paper include present-participial clauses, clauses beginning with `that` as sentence subjects, passive voice, nominalization, and reduced use of some human stance markers. The authors connect instruction tuning, not merely model size, to the concentrated style.

**Humanizer implication:** Do not judge prose by “formality.” Check whether the syntax fits the genre. A client email, news brief, lesson, scene, and research abstract should not share one noun-heavy explanatory voice. Review trailing participles, abstract noun stacks, and passive constructions when they obscure actors or evidence.

Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC11874169/

### Ju, Blix, and Williams, ACL 2025

The Domain Regeneration study prompted models to continue texts from Wikipedia, news, and ELI5, then compared generated continuations with the original domain distributions at multiple levels of syntactic abstraction. The premise is important: “human-like” is not one universal style. A useful model must match the conventions of the actual domain.

**Humanizer implication:** Evaluate against a destination baseline. The same heading density, sentence length, certainty, and vocabulary can be natural in one venue and alien in another.

Source: https://aclanthology.org/2025.findings-acl.120/

## Vocabulary overrepresentation

### Juzek and Ward, COLING 2025

The authors developed a transferable method for identifying abrupt lexical change in scientific English and reported 21 focal words whose increase was likely connected to LLM use. Their paper explicitly says they did not find evidence that model architecture, algorithms, or training data alone explained the overrepresentation. Their model comparisons were consistent with a role for reinforcement learning from human feedback, but the human experiment did not yield a single simple explanation across all focal words.

**Humanizer implication:** Treat a vocabulary list as an empirical watch list, not a theory of causation or a thesaurus task. Density matters more than one occurrence. Repair the underlying abstraction or rhetorical move.

Source: https://aclanthology.org/2025.coling-main.426/

### Kobak et al., Science Advances 2025

The authors analyzed more than 15 million PubMed abstracts from 2010 through 2024. They found abrupt post-ChatGPT increases in hundreds of words; unlike shifts caused by scientific topics or events, many of the new excess words were style-affecting verbs and adjectives. The paper stresses that this is corpus-level analysis and cannot identify whether an individual abstract was processed by an LLM.

The examples highlighted by the authors combine vocabulary such as `meticulously`, `delving`, `intricate`, `interplay`, `comprehensive`, `pivotal`, and `crucial` in dense clusters.

**Humanizer implication:** Flag clusters of ornamental method language and importance claims. Do not label an individual passage from a corpus trend. Replace style words with the method, relationship, constraint, or result they are standing in for.

Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC12219543/

### Geng et al., ACL 2025

Using more than 30,000 papers and 1,000 conference presentations, the study compared changes in written abstracts and spoken presentations by the same broad academic community. It reported increased use of LLM-associated words, including `significant`, in both writing and speech.

**Humanizer implication:** A phrase can spread into ordinary human language after becoming common in model output. That makes static word blacklists less reliable over time and strengthens the case for evidence, genre fit, and author baseline.

Source: https://aclanthology.org/2025.findings-acl.987/

## Pattern drift and human-model coevolution

### Geng and Trotta, ACL 2025

The authors observed that some publicly discussed words, including `delve`, declined in arXiv abstracts after attention to the pattern, while other model-favored words continued to rise. They interpret this as evidence that authors select, edit, or reject model output and that human and model styles coevolve.

**Humanizer implication:** Do not freeze the skill at a 2023 stereotype. Keep dated pattern sets, update them, and prioritize transferable checks such as unsupported interpretation, generic smoothing, source mismatch, and genre mismatch.

Source: https://aclanthology.org/2025.findings-acl.657/

### Sussman and Carter, ACM Web 2025

The study compared a replicated dataset of tweets about the same political figure from 2020 and 2024. It reported shifts toward more positive sentiment and fewer neutral posts in the later sample. This is observational evidence about a corpus, not proof that AI caused each later post.

**Humanizer implication:** Inspect default positivity in contexts that call for neutral description, conflict, mixed evidence, or specific criticism. Do not erase real warmth or optimism merely because positivity can be model-associated.

Source: https://arxiv.org/abs/2504.19556

## Model-specific idiolects

### Sun et al., ICML 2025

The study treated source-model identification as a classification problem across ChatGPT, Claude, Grok, Gemini, and DeepSeek. It reported high held-out classification accuracy and found that model-specific patterns appeared in word distributions and persisted to some degree through rewriting, translation, and summarization.

**Humanizer implication:** “AI style” is not one stable voice. Model-specific artifacts and word distributions can differ, and a rewrite by another model may not erase deeper distributional habits. Use broad craft criteria plus destination-specific artifact checks.

Source: https://arxiv.org/abs/2502.12150

## Human and automated detection limits

### Dugan et al., RAID, ACL 2024

RAID contains more than six million generations across 11 models, eight domains, 11 adversarial attacks, and four decoding strategies. In its evaluation, detectors were vulnerable to adversarial attacks, sampling changes, repetition penalties, and unseen models.

**Humanizer implication:** Do not use a detector score as the success criterion. A style gate should improve truth, specificity, genre fit, reader utility, and voice whether or not a detector changes its output.

Source: https://aclanthology.org/2024.acl-long.674/

### Russell, Karpinska, and Iyyer, ACL 2025

The study asked people to classify 300 nonfiction English articles from humans and several commercial models. Frequent LLM users performed strongly as a group, but individual results varied. Their explanations used both lexical clues and higher-level observations such as formality, originality, and clarity. The paper also tested paraphrased and “humanized” model outputs, illustrating that the evaluation problem changes under editing.

**Humanizer implication:** Skilled holistic reading can notice more than a phrase list, but no single reader’s intuition is infallible. Use multiple evidence layers: provenance, source verification, genre fit, syntax, vocabulary clusters, and author explanation.

Source: https://aclanthology.org/2025.acl-long.267/

### Cheng et al. and Fiedler and Döpke, 2025

Both studies evaluated human and machine identification of AI-written material in bounded datasets. The results cited by Wikipedia show nontrivial error and only modest human recognition in some conditions. These studies are dataset- and task-specific.

**Humanizer implication:** Never turn an editorial observation into an accusation. Ask for the source, draft history, or explanation of a choice when provenance matters.

Sources: https://doi.org/10.1186/s41077-025-00396-6 and https://doi.org/10.1016/j.iree.2025.100321

## Wikipedia participation and tacit norms

### Zhou, Cho, and Terveen, 2025

The authors interviewed 16 Wikipedia editors who had used LLMs. They found different effects for experienced and new editors and emphasized that aligning output with community norms requires tacit knowledge that is especially difficult for newcomers.

**Humanizer implication:** Surface imitation of policy language is not equivalent to competent participation. In any institutional genre, check the real workflow, audience expectations, state transitions, and local terminology. Remove generic assurances of compliance; demonstrate compliance through the actual content and format.

Source: https://arxiv.org/abs/2509.07819

## Editorial implications

The research supports eight operational rules:

1. Match a real genre, not an abstract “professional” voice.
2. Evaluate clusters and structures, not isolated words.
3. Replace abstract praise with actors, actions, conditions, and evidence.
4. Preserve calibrated uncertainty and author stance.
5. Expect model patterns and human language to change over time.
6. Validate citations and platform syntax independently of prose quality.
7. Use provenance and author explanation when authorship actually matters.
8. Measure success by reader utility, source fidelity, and recognizable voice, not a detector score.
