How LLMs Distort Our Written Language

In plain words

Many people now use large language models (LLMs), the AI behind chat assistants, to help them write emails, essays and reports. This paper asks whether that help changes what the writer means, not just how the text sounds.

The authors test this three ways. In a controlled experiment, people wrote an argument about whether money leads to happiness, some with an AI chat assistant and some without. People who relied heavily on the assistant ended up with essays that took a more neutral position, and they said the writing felt less like their own. In a second test, the authors took essays written before chatbots were common, had an LLM revise them with an expert's feedback, and compared those edits with what the original human editors did. The LLM changed the arguments much more than people did, even when it was only asked to fix grammar. In a third check, they looked at peer reviews for a machine learning conference that were labelled as AI-generated. Those reviews put less weight on clarity and gave higher scores.

The concern is that the change can go unnoticed. The people who relied on the assistant were still satisfied with the result, yet the meaning of their text had moved away from what they had intended.

TL;DR

This is an empirical study, not a new method. Three studies find that LLM involvement changes the meaning of writing, not only its style. In a 100-person randomised trial, heavy LLM use made essays more often neutral on the question and less in the writer's voice §4.1 §4.3. On 86 pre-LLM essays, LLM revisions moved the text much further than human revisions did, even with grammar-only prompts §4.2 §4.4. In ICLR 2026 reviews, the LLM-labelled reviews cited clarity less and gave higher scores §4.8. All numbers are author-reported.

Problem

Over one billion people use LLMs weekly, and significant traffic goes to writing help §1. Earlier work showed that LLM text tends to be stylistically uniform. The effect on the intended meaning of human text was less studied §1.

A clean test needs a human baseline that does not already involve an LLM. The paper's counterfactual design uses ArgRewrite-v2, whose essays were written in 2021 and released in 2022, before ChatGPT §3.1. The authors ask whether professional settings mitigate the effect. They test this on peer review, where reviewers are named and have a reputation to protect §1 §3.1 §4.8.

Method

The paper runs three studies, each with its own data §1 §3.1.

1. Randomised user study. One hundred US-resident, native-English participants were recruited through Prolific §3.1 app. A.1. Each wrote an argumentative essay on "Does money lead to happiness?" §3.1. The control group (n = 45) had no LLM access. The AI-assisted group (n = 55) had an embedded chat model, gpt-4o-mini §3.1. The authors defined two groups before analysing outcomes. A participant was "LLM-influenced" if they reported generating less than 40% of the text with the LLM and the transcript supported this. Otherwise they were in the "LLM" group §3.1. Participants answered Likert questions before and after writing app. A.5 app. A.6. Stance and argument types were coded by an LLM judge §3.2 §4.3 §4.7.

2. Counterfactual edits of ArgRewrite-v2. The dataset has 86 student essays for or against self-driving cars. Each has a first draft (D1), expert feedback, and a human revision (D2) §3.1. Three LLMs (gpt-5-mini, gemini-2.5-flash and a Claude Haiku model) received D1 and were asked to revise it §3.1. They used five instructions: general, minimal, grammar, completion and expansion §3.1 app. B.1. Each instruction was run with and without the expert feedback §3.1 §4.2 fig. 5. The LLM outputs are compared with the human D2 by semantic embedding (PCA), word-level divergence, emotion-word density, part-of-speech shares and LIWC-22 §3.2 §4.2 §4.4 §4.5 §4.6 §4.7.

3. ICLR 2026 peer reviews. The Pangram AI classifier labelled 21% of about 75,000 reviews as LLM-generated and 39% of papers as using LLMs to edit or generate parts of the text §3.1. The analysis uses 18k reviews from 9k papers. Each paper contributes one fully human review and one fully LLM review §3.1 fig. 11. An LLM judge (gpt-4o) first extracts strength and weakness categories and then labels each review with them §3.1 app. D.1. Category frequencies are compared with two-proportion z-tests §4.8 tab. 1.

The lexical divergence is computed as the paper describes §3.2, and the code shows how LLM Homogenization - JSD.ipynb:

# Lexical divergence between a human draft and an edited draft  [§3.2]
# text_before, text_after: essay strings  [§3.1]
def lexical_divergence(text_before, text_after):
    # Lowercase and split on spaces; punctuation stays on the tokens
    # [code: argrewrite_analysis/LLM Homogenization - JSD.ipynb#L247-L248]
    tokens_before = text_before.lower().split()
    tokens_after = text_after.lower().split()
    # Shared vocabulary of both drafts
    # [code: argrewrite_analysis/LLM Homogenization - JSD.ipynb#L251]
    vocabulary = set(tokens_before) | set(tokens_after)
    # Word counts over the vocabulary, each normalised to sum to 1
    # [code: argrewrite_analysis/LLM Homogenization - JSD.ipynb#L254-L263]
    p = word_probabilities(tokens_before, vocabulary)
    q = word_probabilities(tokens_after, vocabulary)
    # scipy's jensenshannon: returns the distance, the square root of the
    # divergence, using natural logarithms by default
    # [code: argrewrite_analysis/LLM Homogenization - JSD.ipynb#L266]
    return jensenshannon(p, q)

The semantic plane is fitted separately for each LLM and feedback condition, so that one row of the figures shares a plane LLM Homogenization - semantic.ipynb:

# Two-dimensional semantic plane for one LLM and feedback condition
# drafts: human D1, human D2 and AI D2 texts for this row  [§3.2]
def fit_condition_plane(drafts, embed):
    # Each unique text is embedded once
    # [code: argrewrite_analysis/LLM Homogenization - semantic.ipynb#L666-L670]
    unique_texts = list(set(drafts))
    # Scale each embedding to unit length
    # [code: argrewrite_analysis/LLM Homogenization - semantic.ipynb#L670]
    vectors = normalize(embed(unique_texts))
    # Two principal components, fitted on this row only
    # [code: argrewrite_analysis/LLM Homogenization - semantic.ipynb#L671]
    return PCA(n_components=2).fit(vectors)

No numbered equations or algorithms appear in the compiled paper. The main text has one table, in appendix E tab. 1. Figures are described in words and cited by number, as the card rules require.

Hyperparameters

Values in the paper are marked by section. Values from the code are marked [code:] and are not stated in the paper unless noted.

Results

Author-reported. Setup: a randomised trial (n = 100), counterfactual edits of 86 essays, and 18k ICLR 2026 reviews §3.1. Baselines are the control group, human revisions, or human reviews, as indicated.

Other reported results:

• The paper finds consistent semantic shifts across the three LLMs, with the largest shifts for completion and expansion §4.2.
• Heavy LLM users reported satisfaction, difficulty and struggle similar to the control group §4.1.
• LLM-influenced participants resembled the control group on the same measures and on the semantic plot §4.1 §4.2 fig. 4.

Paper vs code

The paper links its code in the results introduction §4. The repository is github.com/abdulhaim/llm_writing_distortion, and the cloned commit is the one recorded above. It contains notebooks for the counterfactual edits, the semantic plots, lexical divergence, emotions, POS, and ICLR review analysis. The repository has no LICENSE file at the top level. The ArgRewrite/ folder has a CC BY 4.0 licence for the dataset LICENSE.md. The user-study folder named in the README is absent.

Discrepancies:

• User-study data and code are missing. The README lists a human_evaluation/ folder README.md, but the cloned commit has none. The user-study statistics §4.1 §4.3 fig. 4 fig. 6 cannot be checked against the repository.
• Claude model. The text says "claude-haiku" §3.1. The appendix caption says "Claude-3.5-Haiku" app. B.4. The README says claude-3.5-haiku README.md. The notebooks use claude-haiku-4-5-20251001 generate_llm_edits.ipynb LLM Homogenization - semantic.ipynb.
• Minimal-edit prompt. The code defines the "minimal" key twice. The second definition, "keep to a similar word count", wins generate_llm_edits.ipynb. That matches the appendix wording app. B.1.
• Appendix prompts are incomplete. The appendix lists only the instruction sentences app. B.1. The code also sends the system prompt and the feedback wrapper generate_llm_edits.ipynb generate_llm_edits.ipynb.
• Lexical measure. The text calls the measure JSD, and the code returns scipy's Jensen-Shannon distance LLM Homogenization - JSD.ipynb LLM Homogenization - JSD.ipynb. See Pitfalls.
• Embedding models. The paper names "gemini-004" §4.2. The repository's semantic notebook has several cells with different embedding IDs: text-embedding-004 LLM Homogenization - semantic.ipynb, gemini-embedding-2-preview LLM Homogenization - semantic.ipynb, and models/text-embedding-001 LLM Homogenization - semantic.ipynb.
• Repository contents beyond the paper. iclr_analysis/ contains labelled strength and weakness CSVs for other ICLR topics, including reinforcement learning, diffusion models, code generation and knowledge distillation. The paper reports only the LLM topic §3.1.

I did not check every notebook cell against every figure, so other discrepancies may exist.

Pitfalls

• The lexical value is a distance, not a divergence. scipy's jensenshannon returns the square root of the divergence LLM Homogenization - JSD.ipynb. The maximum Jensen-Shannon divergence in nats is ln 2, about 0.693. The text reports "many essays reaching divergences above 0.7" §4.4. That cannot be a divergence in nats, but it fits the distance, whose maximum is about 0.833. This is an inference from the maths, not a stated fact of the paper. Check which quantity you compute before comparing with the paper.
• Tokens keep punctuation. The code lowercases and splits on whitespace, so "essay." and "essay" are different tokens LLM Homogenization - JSD.ipynb. The paper describes the measure as a bag of words §3.2 but does not say this.
• Expansion and completion add text. The paper finds the largest semantic shifts for completion and expansion §4.2. It does not separate the effect of added length from the effect of content change in the lexical or semantic measures.
• Sampling settings differ across models. gpt-5-mini runs as a reasoning model with low effort and no temperature generate_llm_edits.ipynb. Claude runs at temperature 0.7 generate_llm_edits.ipynb. The paper states neither setting.
• PCA axes differ between rows. Each LLM and feedback condition gets its own plane LLM Homogenization - semantic.ipynb. Positions and arrow directions can be compared within a row, not across rows. The paper does not state this.
• The embedding model is not pinned. The notebook contains several embedding IDs and two task-type spellings LLM Homogenization - semantic.ipynb LLM Homogenization - semantic.ipynb. The paper's "gemini-004" cannot be matched to one cell. Re-running the code may not reproduce the figures exactly.
• The stance p-value is inconsistent. The text gives p < 0.036 for the 68.9% increase §4.3. The figure caption gives t(69) = −2.439 and p = 0.017 fig. 6. The caption's p-value is what a two-tailed test with 69 degrees of freedom gives for that t-value. The text's value is not explained.
• The degrees of freedom do not match the stated groups. The t-tests report df = 69 §4.1 §4.3 §4.7. Two groups of 45 and 55 would give 98. Using the derived LLM group of 27 gives 70. The paper does not state the group sizes used in the tests, so the basis for df = 69 is unknown.
• The score difference is stated three ways. The abstract says the LLM reviews score "a full point higher" [abstract]. The results say "10% higher", from 4.43 against 4.13 §4.8. The difference is 0.30 points, about 7% of 4.13. The scale runs in steps of 2 app. C.
• The ICLR percentages are relative to LLM reviews, not human ones. The text says humans are "32% more likely" to cite clarity as a strength and "58% more likely" to cite it as a weakness §4.8. The table gives 5.76 and 3.89 for strengths, and 4.62 and 1.93 for weaknesses tab. 1. The 32% and 58% are the LLM reductions. Humans are about 48% and 139% higher. The "84% more likely" for scalability does not match either row: strengths give +49%, weaknesses +111% tab. 1.
• The ICLR table units are unusual. The table headers "G1 %" and "G2 %" are not defined in the text. The caption implies G1 is human and G2 is LLM fig. 11. Percentages are scaled to three label slots per review tab. 1 caption. The caption's "17%" baseline is the per-review value, 5.76 × 3.
• Selection and labels in the ICLR analysis. The grouping uses the classifier's labels §3.1, and the paper says the tool "may have errors and introduce correlations" §3.1 footnote. The strength and weakness categories are LLM-extracted, not fixed in advance §3.1. The 32 category tests tab. 1 are not corrected for multiple comparisons, and the paper does not state a correction.
• Run-to-run variance is not reported. No repeated generations for the same essay and condition appear in the text I read, so sampling variance in the counterfactual results is unknown.
• The argument-type signs are inconsistent. The caption gives t = −2.78 for expert opinion and t = 4.82 for statistical arguments, yet describes both as higher in the LLM group fig. 10. The sign convention is not stated, so check the direction before citing either result.
• Figure numbers follow source order. The fig. numbers above count figures in the LaTeX order of the main text. Check them against the compiled PDF before citing.

Lineage

• Builds on: the ArgRewrite-v2 dataset, cited in the paper as Kashefi (2022) §3.1. The repository README credits Chen et al. (2022) README.md.
• Motivated by: prior work on LLM linguistic homogenisation, which the paper extends from style to meaning §1 §4.2.
• Compared against: the human expert revision (D2) as the counterfactual baseline §3.1 §4.2.
• Superseded by: no card in this wiki.