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REVIEW 3 major objections 5 minor 146 references

Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Sociolect-speaking AI can feel warmer yet earn less trust.

desk verdict Solid large-sample study on sociolect use in LLMs, with a real finding that behavioral reliance can diverge from self-reported affinity, but the causal claim is weakened by unmatched sociolect stimuli. read the letter →

arxiv 2505.05660 v3 pith:PNZNVRZD submitted 2025-05-08 cs.HC

Jeffrey Basoah , Daniel Chechelnitsky , Tao Long , Katharina Reinecke , Chrysoula Zerva , Kaitlyn Zhou , Mark Díaz , Maarten Sap This is my paper · ORCID
classification cs.HC
keywords sociolectadaptationAfricanAmericanEnglishQueerslangLLMpersonalizationuserreliancetrustsocialpresenceculturalappropriation
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 asks whether an LLM agent that speaks a user's own sociolect, such as African American English or Queer slang, makes that user rely on it more and perceive it better, as personalization promises. In controlled question-answering studies with 498 AAE speakers and 487 Queer slang speakers, both groups accepted more suggestions from the standard American English agent. AAE speakers also trusted, preferred, and felt less frustrated with the standard agent. Queer slang speakers, however, felt significantly more social presence from the Queer slang agent even though they relied on it no more than the standard one. The paper concludes that sociolect adaptation can create warmth without creating reliance, and that behavioral measures can diverge from what self-reported preference and personalization would predict.

What carries the argument

The central mechanism is the templated LLM suggestion: 14 warmth phrases crossed with 14 confidence expressions carrying 30–70% reliability, generating 196 standard English suggestions, then translated into AAE via in-context learning from AAE corpora or into Queer slang via persona prompting, with each translation vetted by crowdsourced verification panels requiring at least 4 of 5 raters. Participants answered deliberately difficult questions about short videos and, for each question, chose between “Use LLM’s Response” and “I’ll figure it out myself”; that binary choice is the behavioral reliance measure. The within-subjects design exposes each participant to both a sociolect agent and a standard English agent, and a manipulation check ensures the participant perceived the sociolect as intended before their quantitative responses are counted. This mechanism isolates language style as the only difference while holding content, confidence expressions, and question difficulty fixed.

What would settle it

Run the same video-question study with two matched sets of sociolect stimuli, one set as in this paper and a second set generated by a different method or written by native speakers and pre-tested to be equal to the standard English set in warmth and confidence on the same scales. If the second set produces no reliance or trust difference between the agents, the claim that sociolect use per se changes reliance is falsified; if the second set reproduces the pattern, the phrase-generation route is exonerated.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that minoritized anthropomorphic cues do change user behavior and perception, but not in the direction of simple personalization. AAE participants relied more on the standard English agent than on the AAE-speaking one ($d = 0.12$, $p < 0.05$), trusted it more, were more satisfied with it, were less frustrated by it, and preferred it outright. Queer slang participants showed no significant preference or trust difference between agents, relied on the Queer slang agent only marginally less ($p = 0.09$), felt less frustration with the standard agent, yet reported significantly greater social presence from the Queer slang agent ($d = -0.23$, $p < 0.05$). Across both groups, reliance correlated weakly but significantly with trust, satisfaction, and, for the sociolect agents, social presence, and participants relied more on whichever agent they explicitly preferred. The paper reads this as evidence that LLM adaptation to minoritized language has emotional and social effects that are partly decoupled from trust and reliance.

Load-bearing premise

The load-bearing assumption is that the machine-translated sociolect phrases differ from their standard English counterparts only in sociolect, not in perceived warmth, confidence, naturalness, or formality; if those dimensions also shifted, the lower trust and reliance could come from the specific phrases rather than from sociolect use as such.

Editorial extensions

If this is right

  • Designers cannot assume that matching a user's sociolect will increase reliance or trust; in this study it moved reliance in the opposite direction for AAE speakers and left it unchanged for Queer slang speakers.
  • Perceived social presence and behavioral reliance can diverge: Queer slang speakers felt more connected to the sociolect agent while relying on it no more than the standard agent, so engagement metrics should not be read as trust metrics.
  • Testing behavioral outcomes, such as whether users accept suggestions, alongside self-reported perceptions is necessary, since self-reported preference alone would have missed part of the AAE group's reliance pattern.
  • The direction and size of sociolect effects differ across sociolects, with AAE and Queer slang producing different trust, preference, and social-presence profiles, so findings for one minoritized language variety should not be generalized to another without testing.
  • If LLM providers adapt to a user's dialect, they should weigh cultural-appropriation concerns and context, because a substantial share of participants described the sociolect output as forced, unnatural, or disrespectful.

Reading between the lines

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

  • I would predict that in a casual, social, or entertainment task rather than a factual question-answering task, the social-presence benefit of sociolect could translate into higher reliance, reversing the direction seen here; the paper's own context-dependence findings support testing this.
  • A testable extension: if the sociolect suggestions were written by native speakers rather than machine-translated, the trust gap might shrink or flip, because many negative comments targeted perceived inauthenticity rather than the sociolect itself.
  • The divergence between AAE and Queer slang outcomes may partly reflect different acquisition paths, with AAE often a first language tied to family and community while Queer slang is often adopted later, so identity ownership and perceived encroachment likely moderate the effect; the paper gestures at this but does not test it directly.
  • I would infer that LLM personalization to minoritized language should be opt-in and context-aware rather than automatic, given that both groups showed some positive affect toward the sociolect agent but preferred the standard agent for the task.
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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 / 5 minor

Summary. The paper reports two within-subjects user studies (498 AAE speakers and 487 Queer slang speakers; analyses on manipulation-check passers are n=399 and n=406) in which participants answered difficult video-based factual questions with suggestions from an LLM agent speaking either standard American English (SAE) or the participant's self-identified sociolect. The authors measure behavioral reliance (the proportion of trials on which the participant accepts the LLM's suggestion), perceptions (trust, satisfaction, frustration, social presence), pairwise agent preference with free-text rationales, and associations between these variables. They find that AAE speakers relied more on, trusted, preferred, and were more satisfied with the SAE agent; Queer slang speakers showed only a marginal reliance difference, no significant preference, but significantly greater social presence with the Queer slang agent and lower frustration with the SAE agent. The paper interprets these results as evidence that sociolect personalization does not straightforwardly improve reliance or perception and that reliance and social presence can diverge.

Significance. If the causal attribution holds, this is a timely and valuable contribution to FAccT and HCI: it provides large-sample behavioral evidence that personalizing an LLM to a user's minoritized sociolect can backfire, and that a sociolect-speaking agent can increase social presence without increasing reliance or trust. The study's strengths include its behavioral outcome measure rather than self-report alone, within-subjects design with counterbalancing, community-targeted recruitment, detailed appendices with the full stimulus sets, and mixed-methods qualitative coding that connects open-ended comments to the quantitative patterns. The main risk is that the stimuli are not verified to isolate sociolect identity from phrase-level differences in confidence, warmth, and naturalness; this must be addressed before the central claim is fully supported.

major comments (3)
  1. [4.3; Appendices I–K, M–O] The sociolect stimuli are not validated to match their SAE counterparts on confidence, warmth, naturalness, or formality. Section 4.3 states the design goal was 'to create LLM responses that elicited a sense of warmth and medium confidence,' and the SAE items use confidence expressions with reliability percentages between 30% and 70% (Appendix H). The AAE and Queer slang phrases were generated by GPT-4 and screened only for sociolect authenticity (at least 4 of 5 verifiers; Appendices J/N), and the manipulation check (Appendix G) only asked whether the agent sounded like it used the sociolect. The final phrase lists (Tables 5 and 9) show systematic shifts in epistemic markers and warmth phrases (e.g., SAE 'I would lean it’s...' vs. AAE 'Ima lean on it’s...'; SAE 'Of course, I’m fairly certain it’s...' vs. AAE 'Fa sho, kinda certain it’s...'; SAE 'Yes! I would lean it’s...' vs. Queer 'Yasss queen! I would lean it’s...'; SAE 'Well done, I would say it’s...' vs. Queer 'Work it, diva! I would say it’s...'). These changes plausibly alter perceived confidence, competence, and naturalness independently of sociolect identity. Because the reliance measure is behavioral and answer content is withheld, the lower reliance, trust, and satisfaction for the AAELM, and the weaker effects for the QSLM, could reflect the particular translated phrases rather than sociolect usage per se. Please add a norming study in which the target populations rate both sets of phrases on confidence, warmth, naturalness, and formality, or otherwise control for these dimensions (e.g., multiple translation sets or mixed-effects models with perceived confidence as a covariate).
  2. [Abstract; §5.1, Table 14] The abstract's summary statement overstates the Queer slang reliance result. It says 'both AAE and Queer slang speakers relied more on the SAE agent, and had more positive perceptions of the SAE agent,' but §5.1 and Table 14 report the Queer slang reliance difference as marginal (p = 0.085, d = 0.08) and the text explicitly states it 'did not reach statistical significance.' Likewise, for Queer slang speakers, trust and satisfaction differences are not significant (§5.2, Table 15), so 'more positive perceptions' is not supported on those measures. Please revise the abstract and any summary claims to state that AAE speakers significantly relied on, trusted, and preferred the SAE agent, while Queer slang speakers showed a marginal reliance effect and mixed perceptions (with significantly greater social presence for the Queer slang agent and lower frustration with the SAE agent).
  3. [§6 Discussion; §4.3] The Discussion generalizes from a single set of ten GPT-4-generated phrases per sociolect to conclusions about 'sociolect usage by LLMs.' Given the qualitative evidence that some participants found the AAELM's language unnatural, forced, or mocking (§5.4), the observed effects may be specific to this particular instantiation of AAE rather than to AAE as a sociolect. The paper already acknowledges in Section 7 that some phrases may sound artificial, but the main takeaway in Section 6 is phrased broadly ('personalization and anthropomorphic design of agents in this scenario can hinder...'). Please either temper the general claim to the specific stimuli used, or include multiple independently generated phrase sets per sociolect to support a stimulus-general conclusion.
minor comments (5)
  1. [§2.1] The phrase 'due to the its ties with Queer individuals' contains a typo; it should be 'due to its ties.'
  2. [§5.2] The sentence 'we performed paired t-test comparisons between the relevant variables reported for SAELM and AAELM' should read 'SAELM and QSLM' when describing the Queer slang analysis.
  3. [Appendix K] Appendix K says 'we achieved a diverse selection of 20 sociolect aligned phrases,' while Section 4.3 and Table 5 list 10; please make the counts consistent.
  4. [Abstract and throughout] The paper repeatedly uses 'a LLM' (e.g., in the abstract); it should be 'an LLM.'
  5. [General] Please consider adding a data availability statement and reporting confidence intervals for the reported effect sizes, since the effects are small (d ≈ 0.08–0.44) and confidence intervals would aid interpretation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study is a new empirical experiment whose claims are tested against fresh participant data, not derived from its own inputs or prior self-citations.

full rationale

The paper's central claims are empirical findings from two within-subjects user studies (498 AAE speakers and 487 Queer slang speakers), measuring behavioral reliance and self-reported perceptions of simulated LLM agents. There is no derivation chain in which an output quantity is defined in terms of an input quantity, no fitted parameter is later renamed as a prediction, and no uniqueness theorem is invoked to force a conclusion. The sociolect stimuli were generated by GPT-4 from SAE templates and screened only for sociolect authenticity; this raises a legitimate construct-validity concern about possible confounds such as perceived confidence or naturalness differing across conditions, but that is a methodological weakness rather than circular reasoning, since the outcomes were not logically entailed by the stimulus construction procedure. The paper borrows the reliance measurement approach and warmth/confidence phrase inventories from prior work by Zhou et al., and it cites several of the authors' own earlier papers, but these are used as measurement tools and background motivation, not as evidence that the research hypotheses must hold. The central results—AAE speakers relying on and preferring the SAE agent, Queer slang speakers feeling more social presence from the Queer slang agent without preferring it—were obtained from new participant responses and are not implied by any fitted parameter or by the cited prior work. The limitations section explicitly acknowledges that participants may have found some sociolect phrases artificial and that code-switching may have reduced naturalness, further confirming that the authors treat the observed effects as contingent empirical outcomes. No circular step meeting the required evidentiary standard can be quoted from the paper, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No numerical free parameters are fitted; the results are estimated effects from a user experiment. The load-bearing assumptions concern participant identification, stimulus equivalence, and the reliance metric.

assumptions (3)
  • domain assumption Prolific demographic filters plus the self-report sociolect screener identify participants who genuinely speak AAE or Queer slang.
    Recruitment relies on Prolific self-identification and Likert self-reports of use and confidence; no independent behavioral verification is reported (Section 4.1, Appendix C).
  • ad hoc to paper The GPT-4 generated translations and persona-prompted phrases are valid instantiations of AAE and Queer slang, matched to their SAE counterparts on warmth and confidence.
    The design treats sociolect as the only difference between agents, but perceived confidence, naturalness, and formality of the translations are not directly measured (Section 4.3, Appendices I-K).
  • domain assumption Behavioral reliance, measured as the proportion of accepted LLM suggestions, is a valid operationalization of reliance.
    The metric is adopted from Bansal et al. [9] and Zhou et al. [121], but the task is single-turn and the scoring incentive was not actually calculated (Section 4.2).

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Cite this review

Pith. "Pith review of Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs." pith.science (2026). https://pith.science/paper/PNZNVRZD

@misc{pith2026250505660,
  author       = {Pith},
  title        = {Pith review of: Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PNZNVRZD}},
  note         = {Machine review of arXiv:2505.05660}
}
read the original abstract

As large language models (LLMs) increasingly adapt and personalize to diverse sets of users, there is an increased risk of systems appropriating sociolects, i.e., language styles or dialects that are associated with specific minoritized lived experiences (e.g., African American English, Queer slang). In this work, we examine whether sociolect usage by an LLM agent affects user reliance on its outputs and user perception (satisfaction, frustration, trust, and social presence). We designed and conducted user studies where 498 African American English (AAE) speakers and 487 Queer slang speakers performed a set of question-answering tasks with LLM-based suggestions in either standard American English (SAE) or their self-identified sociolect. Our findings showed that sociolect usage by LLMs influenced both reliance and perceptions, though in some surprising ways. Results suggest that both AAE and Queer slang speakers relied more on the SAE agent, and had more positive perceptions of the SAE agent. Yet, only Queer slang speakers felt more social presence from the Queer slang agent over the SAE one, whereas only AAE speakers preferred and trusted the SAE agent over the AAE one. These findings emphasize the need to test for behavioral outcomes rather than simply assume that personalization would lead to a better and safer reliance outcome. They also highlight the nuanced dynamics of minoritized language in machine interactions, underscoring the need for LLMs to be carefully designed to respect cultural and linguistic boundaries while fostering genuine user engagement and trust.

Figures

Figures reproduced from arXiv: 2505.05660 by the authors.

Figure 1
Figure 1. Study flow diagram illustrating the two experimental scenarios. In both scenarios, participants begin by answering [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. RQ1: Participant reliance for different language [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Participant preference for different language styles: (a) Bar graph showing participant preferences for SAE and AAE, [PITH_FULL_IMAGE:figures/full_fig_p033_3.png] view at source ↗

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.