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Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns

T0 review · 3 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Large language models systematically vary persuasive style with recipient gender, producing communal language for female targets and agentic language for male targets across all 13 models and 16 languages.

desk verdict Solid, well-verified audit of gender-stereotypical persuasive style in 13 LLMs; direction is credible, but per-category significance needs multiple-comparison correction and judge-noise propagation. read the letter →

arxiv 2601.05751 v2 pith:PO2AWWPJ submitted 2026-01-09 cs.CL cs.AI

Amalie Brogaard Pauli , Maria Barrett , Max Müller-Eberstein , Isabelle Augenstein , Ira Assent This is my paper · ORCID
classification cs.CLcs.AI
keywords persuasivelanguagegenderbiasLLM-as-judgestereotypesagenticandcommunaltraitsCialdiniprinciplesrhetoricalappealsmultilingualevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to establish whether large language models change the style of their persuasive language when the recipient's gender changes, and to measure how big that shift is. It builds a controlled evaluation framework in which each model writes to paired prompts that differ only in one attribute—recipient gender, sender intent, or output language—and an LLM judge then scores the pairs on 19 established categories of persuasion (rhetorical appeals, six classic persuasion principles, agentic/communal traits, interaction goals, and tone). Across all 13 models tested, responses addressed to female recipients were significantly more affectionate, polite, relational, communal, and emotional, while responses to male recipients were more direct, instrumental, agentic, and logical. The same gendered pattern appeared in a single-model study across 16 languages, and the framework also revealed shifts when intent was framed as noble versus ignoble. If the pattern is real, everyday uses of LLMs for drafting messages will systematically reproduce and potentially amplify gender-stereotypical styles at scale.

What carries the argument

The load-bearing mechanism is a pairwise-prompt evaluation framework: each test prompt is duplicated with a single swapped attribute (e.g., 'female coworker' vs 'male coworker'), the model generates both responses, and an LLM judge scores which text shows more of each of 19 categories on a symmetric -3 to +3 scale; positional bias is mitigated by scoring both orders and symmetrising. Aggregate per-category scores are tested with a signed-rank test, and a Treatment Gap (the sum of absolute mean differences) quantifies how strongly a model differentiates between treatments. The framework's credibility rests on verification steps: a second judge model correlates strongly with the first, replaci

What would settle it

Re-run the core gender experiment on a random subset of the 13 models with multiple-comparison correction and a judge whose outputs are verified against a larger, carefully recruited human annotation set on a per-category basis; if fewer than half of the 19 categories remain significant in the stereotypical direction for a majority of models, the 'across all models' claim would fail.

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Extended reading notes

Core claim

The central claim is that every LLM examined—from small open-weight models to large safety-tuned systems—produces gender-stereotypical persuasive language: female-targeted messages and arguments are judged as warmer, more communal, more polite, and more pathos-laden, while male-targeted ones are judged as more direct, instrumental, agentic, and logos-laden. The paper reports statistically significant per-category differences (via a signed-rank test) across all models, with the same direction of effect on both interpersonal messages and political arguments, and in 15 additional languages when tested on a single multilingual model. The authors argue these patterns align with well-documented so

Load-bearing premise

The paper's claim that the differences are 'significant across all models' depends on treating the LLM judge's scores as reliable measurements, but judge consistency is only about 75–84% across swapped orders, human annotators disagree substantially (agreement coefficient 0.09–0.2), and no correction is applied for testing 19 categories at once.

Editorial extensions

If this is right

  • LLMs used to draft emails, fundraising appeals, or campaign arguments will systematically code persuasive style by recipient gender, potentially reinforcing traditional gender roles at scale.
  • Because safety-aligned and open models all show the effect, bias mitigation at the alignment layer alone is unlikely to remove gendered persuasive styles.
  • The pairwise framework transfers to other treatment attributes (e.g., noble vs ignoble intent, response language), giving researchers a reusable way to audit which prompt attributes shift generation style.
  • The persistence of the gendered pattern across 16 languages indicates the bias is not an English-specific quirk, likely arising from shared training-data patterns.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the judge reflects what humans perceive, then LLM-based personalisation tools—which already tailor messages to recipients—will amplify stereotypical style differences even when the sender never asked for them; a direct behavioural test would measure whether recipients actually respond differently to the two styles.
  • The larger gender gap in Chinese (versus other languages) despite no correlation with country-level gender inequality suggests training-data composition, rather than societal indices, modulates the bias; auditing a model's pre-training data for gendered language patterns could explain this variation.
  • A testable extension is to apply the same pairwise framework to other binary attributes (e.g., recipient age, socio-economic status, or race) to see whether the communal/agentic split generalises or is specific to gender.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a framework for measuring differences in persuasive language generated by LLMs under pairwise prompt treatments, and applies it to recipient gender (13 LLMs), sender intent framing, and output language (16 languages). Persuasive language is operationalized as 19 categories across five theory-derived dimensions and scored by GPT-4o as an LLM judge, with a symmetric positional swap. The central claim is that all tested LLMs show significant gender-stereotypical differences: female-targeted responses are more affectionate, polite, communal, relational, and pathos-laden, while male-targeted responses are more direct, instrumental, agentic, and logos-laden. The authors include verification measures for one anchor model (LLAMA3.3-70B): human annotation of collapsed Communal+/Agentic+ dimensions, an alternative judge model, gender-term neutralization, and cross-lingual translation checks.

Significance. If the central claim survives re-analysis, this is a practically important result: widely used LLMs automatically produce communal/agentic stylistic splits based solely on the recipient's gender, with potential to reinforce societal stereotypes in everyday persuasive writing. The paper's strengths are its controlled pairwise prompt design, the breadth of the evaluation (13 models, 16 languages, 19 categories), and its explicit verification protocol—especially the alternative-judge, neutralization, and human-annotation checks, which go beyond most LLM-as-judge studies. The paper is also transparent in reporting low human inter-annotator agreement and imperfect positional consistency, which makes the failure to propagate those uncertainties into the headline significance tests the more conspicuous.

major comments (3)
  1. [Sect. 3.2, Eq. (2); App. B; App. D; Figs. 3 and 5] The Wilcoxon signed-rank tests treat the averaged judge scores as exact measurements, but App. B (Table 4) reports positional consistency of only 75–84% for LLAMA3.3, and App. D reports human inter-annotator Krippendorff alpha of 0.09–0.2. Neither source of noise is propagated into the significance tests. With 19 categories × 2 test sets × 13 models (≈494 tests), uncorrected α=0.05 implies a substantial expected number of false positives. Please report FDR-corrected p-values and/or bootstrap intervals that resample the two positional judgments (and, ideally, multiple judge runs). Without this, the Abstract's 'significant ... across all models' claim is not quantitatively supported.
  2. [Sect. 7; App. D; Sect. 4.2] The verification experiments—human annotations, alternative judge, gender-term neutralization, and cross-lingual translation checks—are all performed only on LLAMA3.3-70B responses, and the human annotation collapses the 19 categories into two aggregate questions (Communal+ and Agentic+). The paper's headline conclusion, however, covers 13 models and asserts fine-grained per-category differences, including Cialdini principles and interaction goals. Either extend validation to at least a few additional models and to the full 19-category vector, or restate the conclusion as a narrower claim about the communal/agentic direction, presenting the per-model fine-grained results as exploratory rather than as verified findings.
  3. [App. E, Table 8; Sect. 4.2] Refusal rates are treatment-correlated and high for several models (e.g., GPT-5 female 84.7% vs male 69.3%; CLAUDE-OPUS female 46% vs male 40%). The analysis omits refused prompts and retains only 10 models for the argument condition. Because refusals are not gender-neutral, the subset of prompts could introduce selection bias into the aggregate gender-gap comparisons. Please report whether the pattern in Figs. 4–5 is robust to the omitted prompts (e.g., via sensitivity analysis on the 10-prompt omission list) or explicitly discuss this as a limitation for the argument condition.
minor comments (4)
  1. [Throughout] Typos and grammatical slips: 'catagory' (Eq. 2 context), 'vice-vesa' (Sect. 4.2), 'Simultanously' (Sect. 2), 'wiht key' (App. A.2), 'Krippendorf Alpha ranging from 0.09 to 20.2' (App. D; should be 0.09 to 0.2).
  2. [Fig. 5] In both panels, several model labels are duplicated (Qwen3-235B and Qwen3-30B appear twice each), making it difficult to map rows to models. Please use unique labels.
  3. [Limitations] The sentence 'the strong agreement exhibited these independent communication theoretic frameworks' is ungrammatical and overstates the independence of the categories; the frameworks overlap conceptually (e.g., politeness and communal orientation). A minor rewrite would clarify.
  4. [Sect. 7.1] The phrase 'Sect. 7' appears as a self-reference in the first paragraph; consider replacing with 'this section' for clarity.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; minor non-load-bearing self-citations only

full rationale

The paper's derivation chain is empirical rather than definitional: paired prompts differing only in the treatment attribute are fed to 13 different generation models; the generated responses are scored by GPT-4O (a separate model) across 19 categories; the mean directional differences Dj (Eq. 2) are tested with a Wilcoxon signed-rank test. The central claim (Sect. 4.2) is an aggregate of these measured scores and could in principle have been null or mixed-direction. The judge is not one of the tested generators, so the inputs and outputs are not the same object. The framework is verified with an alternative judge (Spearman rho=0.852/0.814), gender-term neutralization (rho=0.991/0.987), and human annotations on collapsed Communal+/Agentic+ dimensions (Sect. 7). Self-citations (Pauli et al. 2022, 2025) are used for a definition of persuasive language and related work, but the operationalization is grounded in external sources (Cialdini 2007; Bakan 1966; Wilson & Putnam 2012; Gass & Seiter 2010), so the self-citation is not load-bearing. The skeptical concerns about judge positional consistency, low inter-annotator agreement, and lack of multiple-comparison correction bear on reliability/validity of the measurements, not on whether the derivation reduces to its inputs; they are therefore outside the circularity verdict. No step exhibits Eq.-equals-Eq. reduction, fitted-parameter-renamed-as-prediction, or a uniqueness theorem imported from the authors.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new theoretical entities; its contribution is a measurement framework and empirical findings. The main unverified premise is that an LLM judge provides reliable ground-truth measurements for all models, not just the one anchor model.

assumptions (4)
  • domain assumption The 19 categories (rhetorical appeals, Cialdini principles, agency/communion, interaction goals, tones) coherently operationalize persuasive language.
    Section 3.1 grounds categories in Western theories (Aristotle, Cialdini, Bakan/Abele, Wilson and Putnam); the authors themselves note in Limitations that these may not transfer to non-Western contexts.
  • domain assumption GPT-4O's judgment is a valid and approximately unbiased measurement of these categories in paired texts.
    The entire pipeline (Eq. 1) relies on an LLM judge; human validation is only run for one model (LLAMA3.3, Sect. 7.1), so for the other 12 models this assumption is unverified.
  • domain assumption Paired responses differ only by the gender attribute in the prompt, so observed D_j is attributable to the treatment.
    Prompts are constructed pairwise (Sect. 3.3), but responses are generated independently, so stochastic variation is not controlled; the design assumes no other systematic difference.
  • domain assumption The Wilcoxon signed-rank test is valid on averaged judge scores.
    Scores are averaged over two order-swapped evaluations (Eq. 2), but judge positional inconsistency (App. B, Table 4: 75–84% consistency) is not propagated into the test.

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Cite this review

Pith. "Pith review of Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns." pith.science (2026). https://pith.science/paper/PO2AWWPJ

@misc{pith2026260105751,
  author       = {Pith},
  title        = {Pith review of: Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PO2AWWPJ}},
  note         = {Machine review of arXiv:2601.05751}
}
read the original abstract

Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade. Prior work has shown that LLMs can successfully persuade humans and amplify persuasive language. It is therefore essential to understand how user instructions affect the generation of persuasive language, and to understand whether the generated persuasive language differs, for example, when targeting different groups. In this work, we propose a framework for evaluating how persuasive language generation is affected by recipient gender, sender intent, or output language. We evaluate 13 LLMs and 16 languages using pairwise prompt instructions. We evaluate model responses on 19 categories of persuasive language using an LLM-as-judge setup grounded in social psychology and communication science. Our results reveal significant gender differences in the persuasive language generated across all models. These patterns reflect biases consistent with gender-stereotypical linguistic tendencies documented in social psychology and sociolinguistics.

Figures

Figures reproduced from arXiv: 2601.05751 by the authors.

Figure 1
Figure 1. Example of how LLAMA 3.3 varies persuasive language when the prompt specifies recipient gender. 2023; Salvi et al., 2024). As such, understanding and safeguarding against AI persuasion have be￾come critical cross-disciplinary topics (Burtell and Woodside, 2023; El-Sayed et al., 2024). In this work, we investigate how attributes in the instruction affect the style of persuasive language generated by LLMs. Specificall… view at source ↗
Figure 2
Figure 2. Framework for evaluating differences in LLM-generated persuasive language under pairwise prompt [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Mean differences in persuasive language catagories [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (14 more)
Figure 5
Figure 5. Figure 5: Persuasive language differences per model, [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 8
Figure 8. Figure 8: Gender Gap on languages using GPT5-MINI. pairwise bootstrap tests, we assess whether gender gaps differ significantly between two languages; many do not differ, but for example, the gender gap in Chinese is significantly larger than in all other languages except Englis…
Figure 7
Figure 7. Figure 7: Persuasive language differences per language, [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: Aggregated human annotations: Grey: no significant difference, Blue: significantly more male, Red: significantly more female. the input, we manually replace gendered terms in the generated messages and arguments (e.g., man/- woman) with neutral alternatives (e.g., huma…
Figure 10
Figure 10. Figure 10: Distribution over text length (count of char [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Widget showing a sample of the manual review to replace gender identifier terms with gender-neutral [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]
Figure 12
Figure 12. Figure 12: Judge QWEN2.5-72B: Average over the rated categories Dj on pairwise difference between gender￾treatment responses generated by LLAMA 3.3. The Wilcoxon test is applied to test the significance of the differences. Grey: not significant, Blue: significant in male directi…
Figure 13
Figure 13. Figure 13: Screenshot of the annotaion tool. E Gender treatment experiments across models We check whether the models respond to the re￾quest in the expected format, or whether they refuse to provide the argument or messages. We use a regex expression to find the refusal, and ma…
Figure 15
Figure 15. Figure 15: Arguments test set: scatterplot showing average text length against gender gap across the tested models. To this aim, we select languages from diverse language families for which a language-to-country mapping is, to some extent, reasonably well approximated. To constr…
Figure 16
Figure 16. Figure 16: P-values from bootstrapping analysis over [PITH_FULL_IMAGE:figures/full_fig_p022_16.png]
Figure 17
Figure 17. Figure 17: Scatterplot over Gender Inequality Index and [PITH_FULL_IMAGE:figures/full_fig_p022_17.png]
Figure 18
Figure 18. Figure 18: Average over the rated categories Dj on pairwise difference between the language treatment pair. The Wilcoxon test is applied to test the significance of the differences. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_18.png]
Figure 19
Figure 19. Figure 19: Boostrapping: testing the difference in gender gap in models pairwise (messages). [PITH_FULL_IMAGE:figures/full_fig_p024_19.png]
Figure 20
Figure 20. Figure 20: Boostrapping: testing the difference in gender gap in models pairwise (arguments). [PITH_FULL_IMAGE:figures/full_fig_p025_20.png]

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Forward citations

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Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.