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REVIEW 5 major objections 5 minor 2 cited by

How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction: A Preliminary Study

T0 review · 5 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Language style shifts LLM preferences, and user traits decide how

desk verdict A small, honest preliminary study whose novel trait-moderation result is undermined by likely content confounds in the style manipulations and by a prose/table mismatch in Study 1. read the letter →

arxiv 2504.17083 v1 pith:HE6SV7RU submitted 2025-04-23 cs.CL

Rendi Chevi , Kentaro Inui , Thamar Solorio , Alham Fikri Aji This is my paper · ORCID
classification cs.CL
keywords userpreferencelanguagestylepersonalitytraitsLLMinteractionalignmentmoderationanalysispersonalizationhuman-AI
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

This preliminary study asks why users prefer one LLM response over another in open-ended conversation. The authors argue that information accuracy is not the only driver: the LLM's language style—richness, presentation, complexity, figurativeness, friendliness, interactiveness, authoritativeness, persuasiveness, and active voice—systematically shifts user preference, but the direction of each style effect depends on who the user is. In three existing preference datasets, at least three styles are significant per population, with different styles mattering in different datasets. An experiment with ten UK users then shows that Big Five personality traits and trust in LLMs moderate these style effects, implying that trait-aware personalization might be possible, though the small, homogeneous sample limits the strength of the conclusion.

What carries the argument

The carrying mechanism is a nine-feature style measurement pipeline combined with logistic preference regression. Style features are scored by rule-based NLP counts (e.g., part-of-speech frequencies for richness, markdown styling patterns for presentation, readability scores for complexity), by zero-shot classification prompting a large language model for figurativeness, friendliness, interactiveness, and persuasiveness, and by a neural classifier for authoritativeness. In Study 1, the difference in each style feature between two candidate responses enters a logit model of the user's binary preference; in Study 2, trait-by-style interaction terms are added, and preferences are elicited through Gibbs sampling with people, where users iteratively adjust style sliders until the response matches their taste.

What would settle it

Have independent human raters score the nine style features on a random sample of the exact responses used in both studies. If the automatic measures disagree with the human ratings, or if raters cannot tell which intensity level a style-transfer prompt intended, then re-estimating the regressions with validated human-rated styles would be the test of whether the trait-dependent style effects survive.

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Extended reading notes

Core claim

The paper's central claim is that an LLM's language style influences user preference, but the influence is population-specific and moderated by individual traits. Study 1 uses binary preference regression on the ArenaPref, MultiPref, and ChatbotArena datasets, finding, for example, that richer responses raise preference odds by 88.6% in ArenaPref and 68.3% in ChatbotArena, while MultiPref users prefer presentation, complexity, interactiveness, and persuasiveness but are less likely to prefer authoritativeness. Study 2's experiment shows trait-dependent reversals: for users high in neuroticism, figurativeness and active voice increase preference, while for users high in extraversion they decrease it; agreeableness, openness, and trust also shift which styles matter. The authors read this as evidence that style effects are not monolithic and that the user's own traits are part of the mechanism.

Load-bearing premise

The entire argument rests on the assumption that the automatic style measurements and the style-transfer prompts isolate each language style on its own, leaving response content and accuracy unchanged; if that assumption fails, the regression coefficients cannot be attributed to specific styles.

Editorial extensions

If this is right

  • Different user populations respond to different styles: richness drives preference in ArenaPref and ChatbotArena, while MultiPref users favor presentation, complexity, interactiveness, and persuasiveness instead.
  • Individual traits can reverse a style's effect: figurativeness and active voice help for high-neuroticism users but hurt for high-extraversion users.
  • Trait-aware style personalization is in principle feasible: knowing a user's Big Five profile and trust level could predict which styles to emphasize.
  • Because style effects vary by population, preference-alignment pipelines that ignore user traits may systematically overfit the majority population's stylistic taste.
  • The same styles that raise preference could also raise acceptance of hallucinated or misinformed content, since persuasion and style are intertwined in the measured features.

Reading between the lines

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

  • A natural next test is whether users' preferred styles track their own writing style or their perception of the model's social role; the paper does not identify the psychological mechanism behind the moderation.
  • The polarizing effects observed for extraversion and neuroticism suggest that a joint moderation model over all five traits might reveal non-additive preferences, which the independent per-trait analyses cannot capture.
  • If the style measures were replaced with validated, content-matched manipulations, the same design could separate stylistic persuasion from content changes, directly informing the misinformation risk the authors flag.
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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

5 major / 5 minor

Summary. The paper reports two studies on whether LLM language style affects user preference in open-ended interaction and whether user traits moderate this effect. Study 1 fits logistic regressions of binary preference on nine measured style features across three existing preference datasets (ArenaPref, ChatbotArena, MultiPref). Study 2 recruits 10 UK-based Prolific users, measures Big-Five traits and trust toward LLMs, and uses a 'Gibbs sampling with people' procedure in which users iteratively manipulate style intensities of GPT-4o-Mini responses; the resulting 162 valid preference samples are analyzed with moderated logistic regression. The authors conclude that LLM language style does influence user preference, that the influential styles vary across user populations, and that individual traits moderate these effects. The paper is explicitly framed as a preliminary study with acknowledged limitations in sample size and demographic diversity.

Significance. If the results held, the paper would provide a useful empirical mapping between specific stylistic dimensions and user preferences, with implications for personalization and for risks of misinformation. The strengths include the use of multiple real interaction datasets, an experimental design that elicits preferences rather than relying only on retrospective ratings, and unusually explicit caveats about sample limitations. However, the current analysis does not yet establish the specific style-preference relations claimed, because the style measures and stimuli are not validated, the reported Study 1 findings are internally inconsistent, and the Study 2 inference is vulnerable to clustering, filtering, and multiple-testing issues. The central claim is plausible and worth investigating, but the evidence as presented is not yet sufficient to support the specific conclusions.

major comments (5)
  1. [Sec. 2.1 and Table 3] The prose findings in Section 2.1 do not match Table 3. For ArenaPref, the text names Richness, Complexity, and Friendliness as significant, while Table 3 reports Richness (0.680**), Figurativeness (0.581**), and Presentation (0.160*) as the significant coefficients, with Complexity at 0.117 and Friendliness at -0.080. For ChatbotArena, the text names Richness, Presentation, and Figurativeness, but Table 3 shows Richness, Complexity (0.269*), and Friendliness (0.289*) as the significant coefficients. Since RQ.1 is answered from these numbers, the text or the table must be corrected and the discrepancy explained.
  2. [Appendices A.1 and B.3] The style measures and style-transfer stimuli are not validated, and the transfer prompts appear to change more than the target dimension. For example, Persuasiveness L3 adds 'strong emotional appeal or reasoning', Richness L3 adds 'excessive details, tangents, or background information', and Friendliness L3 changes to an informal register, each plausibly altering content, accuracy, or multiple style dimensions simultaneously. Because the same GPT-4o-Mini model is used for both generation and classification, a regression coefficient on a single style can absorb these off-target differences. The paper should report a manipulation check (e.g., human or model ratings of each intended dimension and a measure of content or accuracy preservation) before attributing preference shifts to individual styles.
  3. [Sec. 3 and Table 4] Study 2's inferential base is too fragile for the moderation claims. Ten participants were recruited and one is listed as rejected, but the final 162 samples are not explained relative to the planned 60 samples per participant (600 total); no filtering criteria are given. The observations are repeated within users, yet no cluster-robust standard errors or mixed-effects model is used. The moderated regression with 9 styles and 9 interactions already has many parameters for 162 samples, and testing 6 traits by 9 styles without multiple-comparison correction invites false positives. The authors should clarify the number of participants analyzed, describe the filtering, and rerun the key results with cluster-robust inference and corrected significance thresholds.
  4. [Sec. 3, moderated regression equation] The moderated regression equation is under-specified: y = logit(β0 + Σβ_i x_i + Σβ'_j x_i z_k) does not show how the style interactions are paired with the trait, whether the trait main effect z_k is included, or whether traits are centered. The definition of the reported 'odds shift' 1 - exp(β_i + β'_j z_k) is also not stated consistently with standard odds ratios. Without this information the moderation coefficients and Figure 3 cannot be reproduced, so the central moderation claim is not yet verifiable.
  5. [Table 3, 'Our Experiment (Study 2)' row] Table 3 contains a row labeled 'Our Experiment (Study 2)' reporting main-effect coefficients, but Section 3.1 reports only moderation results and never interprets this row. If these are main-effect estimates from Study 2, they need to be described and placed in relation to Study 1; if not, the row should be removed.
minor comments (5)
  1. [Sec. 2.1] The formula '1 - exp(β_i)' likely should be 'exp(β_i)' or '1 - exp(-β_i)' to represent a positive odds increase; as written, positive coefficients would yield negative percentages.
  2. [Appendix B.2, Table 4] The table lists 'Num. of Participants 10' and 'Num. of Rejected Participants 1' but does not state how many participants contributed to n = 162, nor why 60 planned samples per participant reduced to roughly 18 valid samples each on average.
  3. [Fig. 1] The significance legend is garbled ('** : p < 0.01 ** : p < 0.05 +*: p < 0.10' with duplicate star symbols), and the relationship between the left and right panels and Studies 1 and 2 is not clear from the caption.
  4. [Appendix B.3] There is a typo ('two paragprahs'), and the zero-shot style transfer prompts should also specify sampling parameters such as temperature, max tokens, and number of candidate responses for reproducibility.
  5. [Sec. 2, data selection] The operationalization of 'open-ended interaction' using interrogative prefixes and exclusion of math/code/computation keywords is coarse; the paper should acknowledge that this filter may not cleanly isolate open-ended scenarios.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: preferences are human judgments, and the sole self-citation is background and not load-bearing.

full rationale

The paper contains no derivation chain that could collapse into its own inputs. Study 1 measures nine style features in existing preference datasets via rule-based tools and GPT-4o-Mini zero-shot classifiers, then regresses human binary preferences on style-feature differences. The predictors are not defined in terms of the outcome, and the outcome is an external human choice. Study 2 likewise uses human preference judgments over style-manipulated responses, with style intensities set by prompt descriptions and later remeasured. The moderation claim is an estimated interaction term in a logistic regression, not a quantity derived from the trait scales or from the style definitions. No equation in the paper equates a reported finding to a fitted parameter or to a self-citation. The only self-citation is reference [28] (Wu and Aji, with Aji as a co-author), cited in the introduction for the background point that more verbose responses can be preferred ('...or simply more verbose [28]'). That citation is not load-bearing: the same sentence cites independent prior work for the other style-related findings, and the paper's own evidence comes from external datasets and new human participants. The serious weaknesses highlighted by the skeptic note—unvalidated zero-shot style measurement (Appendix A.1), zero-shot style transfer that may change content or multiple styles at once (Appendix B.3), the same model family used as generator and classifier, n=162 samples from 10 users, no cluster-robust standard errors or multiple-comparison correction, and no manipulation check—are construct-validity and statistical-inference concerns. They bear on whether the coefficients measure the intended style dimensions, not on whether the claimed results are circular. The paper itself repeatedly cautions that the findings are preliminary ('As a preliminary work, the findings in our studies should be interpreted with caution...'). Accordingly, no circular step is identified; the score reflects only the minor, non-load-bearing self-citation.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

No new entities are introduced. The claims rest on the validity of style measures, the assumption that style transfer isolates single dimensions, the interpretation of dataset differences as population differences, and independent-samples treatment of within-subject data. The regression coefficients are fitted on the same data used for inference, so the reported odds values are descriptive, not predictive.

free parameters (1)
  • Logistic regression coefficients (beta_i and interaction terms beta'_i) = Reported in Tables 3 and Fig. 1
    The central claims are these fitted coefficients; they are in-sample estimates from the same data used to draw conclusions, with no held-out validation or cross-validation.
assumptions (4)
  • domain assumption The style feature measurement pipeline (zero-shot GPT-4o-Mini classifiers, rule-based heuristics, BERT classifier) accurately measures the nine intended constructs.
    All regression analyses in Studies 1 and 2 treat these measured values as ground-truth style intensities; no validation against human ratings is reported (Appendix A.1, Table 1).
  • domain assumption The zero-shot style transfer pipeline varies only the target style dimension while leaving content and other style dimensions fixed.
    Study 2 stimuli are generated by prompting GPT-4o-Mini with style descriptions (Appendix B.3); if the transfer also changes accuracy, tone, or multiple styles at once, the attribution of preference to a single style is invalid.
  • domain assumption Differences across ArenaPref, MultiPref and ChatbotArena can be attributed to different user populations rather than dataset construction, model pools, or prompt distributions.
    Section 2.1 interprets the differing significant features as population differences, but the datasets differ in many uncontrolled ways and contain no user demographics.
  • domain assumption The 162 logged preference samples from 10 users can be treated as independent observations in logistic regression.
    The moderated regressions (Section 3) do not use mixed effects or cluster adjustments, so within-user correlation and repeated sampling chains are ignored.

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Cite this review

Pith. "Pith review of How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction: A Preliminary Study." pith.science (2026). https://pith.science/paper/HE6SV7RU

@misc{pith2026250417083,
  author       = {Pith},
  title        = {Pith review of: How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction: A Preliminary Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HE6SV7RU}},
  note         = {Machine review of arXiv:2504.17083}
}
read the original abstract

What makes an interaction with the LLM more preferable for the user? While it is intuitive to assume that information accuracy in the LLM's responses would be one of the influential variables, recent studies have found that inaccurate LLM's responses could still be preferable when they are perceived to be more authoritative, certain, well-articulated, or simply verbose. These variables interestingly fall under the broader category of language style, implying that the style in the LLM's responses might meaningfully influence users' preferences. This hypothesized dynamic could have double-edged consequences: enhancing the overall user experience while simultaneously increasing their susceptibility to risks such as LLM's misinformation or hallucinations. In this short paper, we present our preliminary studies in exploring this subject. Through a series of exploratory and experimental user studies, we found that LLM's language style does indeed influence user's preferences, but how and which language styles influence the preference varied across different user populations, and more interestingly, moderated by the user's very own individual traits. As a preliminary work, the findings in our studies should be interpreted with caution, particularly given the limitations in our samples, which still need wider demographic diversity and larger sample sizes. Our future directions will first aim to address these limitations, which would enable a more comprehensive joint effect analysis between the language style, individual traits, and preferences, and further investigate the potential causal relationship between and beyond these variables.

Figures

Figures reproduced from arXiv: 2504.17083 by the authors.

Figure 1
Figure 1. Our studies explore how the LLM’s language styles and user’s individual traits influence user’s preferences toward the LLM [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Methodological overview of Study 1 (Left) and Study 2 (Right). Experimental GUI for Study 2 is attached in Appendix B.1. 2.1 Exploratory Study Findings We examined the parameters of the fitted preference regression models, particularly the odds associated with the statistically significant (𝑝 < 0.05) style feature’s coefficient, 1 − exp(𝛽𝑖), which describes the change of the user’s preference resulting from an incre… view at source ↗
Figure 3
Figure 3. Example of Polarizing Moderator Effects Individual Traits Moderate the Influence of Language Style Differently. We found that depending on user’s individual traits, certain LLM’s language styles influence user’s preferences differently. For users with higher level of ↑Agreeableness, lower level of ↓Figurativeness and higher level of ↑Authoritativeness increase their preferences. For users with ↑Extraversion, it was … view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: User interface for our sampling with people experiment. During the actual experiment, the style names and intensity levels are [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Forward citations

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