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REVIEW 3 major objections 4 minor 150 references

Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Writers who meet the AI in Spanish first use its English drafting feature roughly 64% less, a violation of the rational-choice axiom that choices in one language should not depend on experience in another.

desk verdict The headline choice-independence claim is not supported by the reported analysis; the second experiment is the more solid contribution. read the letter →

arxiv 2502.09532 v1 pith:V7JJVIVM submitted 2025-02-13 cs.CL cs.AIcs.HC

Shreyan Biswas , Alexander Erlei , Ujwal Gadiraju This is my paper
classification cs.CLcs.AIcs.HC
keywords Human-AIinteractionChoiceindependenceMultilingualLLMsUserrelianceCo-writingPersuasivewritingCharitablegivingAlgorithmaversion
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 paper reports two experiments asking whether people use a multilingual LLM writing assistant independently across languages. It finds they do not: writers who first used the assistant in Spanish used its AI text-generation feature roughly 64% less when writing in English afterward, even though the underlying model was unchanged. The result is offered as evidence that choice independence, a standard assumption of rational choice theory, is violated in an applied co-writing setting, extending earlier lab-only demonstrations. A second experiment on charitable giving finds that the usage gap did not change how persuasive the ads were, but donors' beliefs about whether an ad was written by AI did affect giving, especially among Spanish-speaking women.

What carries the argument

The central mechanism is the order-of-exposure design built into a customized version of the co-writing tool ABScribe, which offers an AI Drafter feature that generates new text from a user prompt. Writers in two bilingual conditions wrote charity ads in English and Spanish in opposite orders, and the count of AI Drafter uses in the second-language task served as the revealed-utility measure. The paper supplements usage counts with a weighted-average embedding similarity between AI-generated segments and the final ad, and with benchmark checks showing the underlying LLaMA 3.1 model follows instructions worse in Spanish than English. The independence axiom of expected utility theory supplies the normative baseline: if writers evaluated languages independently, usage in the second task should not depend on which language came first.

What would settle it

Run the same bilingual writing study with an additional English-then-English condition. If writers in the second English session also cut AI Drafter usage by roughly 60%, the observed gap is an order or fatigue effect rather than language transfer; if English-second usage stays near English-first levels, the choice-independence interpretation is supported.

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Extended reading notes

Core claim

The paper's central claim is Result 1: prior exposure to the Spanish version of the LLM assistant reduces subsequent utilization of the English version, a large drop in AI Drafter usage from 28 total uses in the English-first condition to 10 in the Spanish-first condition. The authors interpret this as a violation of choice independence, arguing that users extrapolate the assistant's lower Spanish quality to its English performance and adjust reliance accordingly. The reverse pattern is also reported: participants who met the English assistant first used the Spanish assistant more. The downstream experiment adds two further findings: utilization differences do not change aggregate donation outcomes, and donors cannot reliably tell human from AI ads but condition their giving on what they believe, with Spanish-speaking female participants showing the strongest negative reaction to perceived AI authorship.

Load-bearing premise

The claim that the 64% drop in English AI Drafter use was caused by prior Spanish exposure assumes that no order, fatigue, or learning effect explains lower usage in the second writing task; the design has no English-then-English control showing usage would stay high without Spanish first.

Editorial extensions

If this is right

  • If the finding replicates, developers of multilingual writing tools should expect usage in one language to depend on users' experience in another, so cross-linguistic quality gaps can suppress adoption even where the model is strong.
  • Persuasiveness results imply that utilization drops of this size need not degrade output quality in charity-ad writing, because donations did not differ across treatment conditions.
  • Because most donors could not distinguish human from LLM ads yet responded to perceived AI authorship, disclosure or labeling decisions can directly affect charitable giving.
  • In the authors' sample, the belief effect is concentrated in Spanish-speaking women, who donated 22% less and were nearly four times more likely to donate nothing when they believed an AI wrote the ad; other groups showed weaker, non-significant versions of the same pattern.
  • Rolling out multilingual assistants without monitoring post-deployment language-specific quality could create second-order inequality, since lower-resource-language users may generalize poor experiences and under-use the tool in higher-resource languages as well.

Reading between the lines

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

  • If the observed drop is causal, a plausible mechanism is that users form a holistic quality impression of the assistant rather than separate per-language beliefs; the paper does not directly measure such beliefs, so this remains an inference.
  • A natural testable extension would vary the linguistic and resource distance between first and second language, for example comparing Spanish with a typologically distant low-resource language, to see whether generalization grows with perceived similarity.
  • The donor-belief results suggest that a simple intervention, such as announcing human involvement in drafting or not disclosing AI use, could move donation totals in specific demographics; this follows from the paper's findings but was not tested as a treatment.
  • The design could be extended to repeated interactions over time to check whether the utilization gap decays with learning or persists, which the authors flag as outside their one-session scope.
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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 reports two preregistered experiments on multilingual human-AI co-writing. In Experiment 1, bilingual writers (n=16 per condition) use an LLM-based writing assistant (ABScribe) to produce charity advertisements for the WWF in English and Spanish, with the language order manipulated (ENG_ESP vs. ESP_ENG) plus no-LLM controls. The main reported finding (Result 1) is that writers exposed to the Spanish LLM first use the AI Drafter feature less in the subsequent English task (a drop of roughly 64%, from 28 to 10 total uses), which the authors interpret as a violation of choice independence. A secondary analysis suggests the reverse effect for Spanish when English is experienced first. Experiment 2 (n=720) evaluates the persuasiveness of the resulting ads in a donation task, finding no effects of the utilization differences on donations, but reporting that participants cannot reliably identify AI-generated ads and that Spanish-speaking female participants who believe they read an AI-generated ad donate significantly less. The paper claims to be the first to extend choice-independence violations from abstract lab tasks to an applied multilingual co-writing setting.

Significance. If the causal interpretation of Result 1 holds, the paper makes a meaningful contribution by showing that users' experiences with an LLM in one language spill over to their reliance on the same model in another language, with practical implications for the global deployment of multilingual assistants. The work has several concrete strengths: both experiments are preregistered; the code and data are made openly available; the language-performance assumptions are checked with external benchmarks (Multi-IF, PAWS-X, persuasion detection); and the downstream persuasion experiment with 720 participants is a valuable attempt to connect utilization changes to real-world outcomes. The paper is also explicitly an empirical extension with new data rather than a circular argument. However, the central causal claim rests on a between-group comparison that confounds language order with task position, and the reported analyses do not include the interaction test that would isolate a choice-independence violation.

major comments (3)
  1. [§5.1, Result 1 and Figure 4] The two t-tests used to support Result 1 are separate between-group comparisons on n=16 per group (ENG_1 vs. ENG_2: t=2.2, p=0.04; ESP_1 vs. ESP_2: t=2.58, p=0.017). These tests are not corrected for the multiple feature and language comparisons examined in the same section, and they do not exploit the within-subject pairing that the crossover design provides. The reported significance of the simple effects does not establish a differential effect of order by language; the absence of an interaction test is a particular problem because the observed pattern (both English and Spanish counts lower in Group B than in Group A) is exactly what would be expected from a stable participant-level difference in AI usage. A proper mixed-model analysis of the raw pair-level usage counts is required before the utilization drop can be attributed to prior exposure to Spanish.
  2. [§4.1.3 / §5.1, weighted average similarity] The weighted average similarity measure is used as a second piece of evidence for Result 1, but it is not validated against ground-truth text provenance. The paper reports that ENG_1 advertisements are 14.7% more similar to AI-generated text than ENG_2, using three embedding models, but it does not show that this metric discriminates AI-written from human-written text in this domain, nor does it report calibration or reliability across the two languages. Without such validation, the similarity difference may reflect topic, style, or length differences between first- and second-position writing tasks rather than differential reliance on AI-generated content. The authors should either validate the measure (e.g., against known human-only and LLM-only texts) or present it as a descriptive, non-causal auxiliary result.
  3. [§6.2, Caveats and Limitations] The limitations section acknowledges the small number of writers and the difficulty of recruiting bilingual participants, but it does not explicitly acknowledge the design-level confound between language order and task position, nor the fact that no same-language-order control condition was included. Given that these issues directly qualify the paper's headline result, the limitation should be stated transparently and the appropriate interaction analysis should be reported in the main text rather than only in future-work remarks. The current presentation of Result 1 as 'consistent with violations of choice independence' is stronger than the design and reported statistics support.
minor comments (4)
  1. [§5.2, Result 2] The sentence 'Average donations do not differ between treatments, providing any evidence for a detrimental effect of choice independence violations on persuasion' is contradictory as written; it should read 'providing no evidence' or 'we find no evidence'. The subsequent text correctly reports the absence of an effect, so this is a wording error that should be corrected.
  2. [§6, RQ2 and RQ3 discussion] The paper uses the phrases 'moderate evidence', 'minor evidence', and 'strong evidence' without defining an evidentiary threshold or reporting effect sizes. I recommend stating the chosen standard (e.g., based on p-values or Bayes factors) or replacing these qualitative labels with the actual estimates and confidence intervals.
  3. [References and appendix] Some placeholders remain unresolved: the regression results in §5.2 are referenced as 'Tables ?? and ??', and Table 5 is said to be 'available on the next page'. In addition, references [90]/[91] and [129]/[130] appear to be duplicated with different citation keys. These should be cleaned up before publication.
  4. [Figure 9] The label 'Average time spent outside' is unclear; I assume it means time spent outside the writing platform (e.g., in other browser tabs), but this should be defined in the figure caption or text, and it is unclear how this was measured.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the load-bearing results are direct empirical measurements of AI-drafter usage compared across language-order conditions, with external benchmarks and no fitted parameter renamed as a prediction.

full rationale

The paper's central claims are empirical observations of logged interaction counts, not quantities derived from fitted parameters or from the cited prior work. The key comparison (ENG_1 vs ENG_2: 28 vs 10 AI-drafter uses, t=2.2, p=0.04) is a direct measurement of user behavior; no parameter is fit to the outcome and then relabeled as a prediction. The benchmarks used to document English-Spanish performance gaps (Multi-IF, PAWS-X, and a persuasion-detection dataset) are external instruments with fixed scoring procedures, and Experiment 2's donation game is a separate outcome measure collected independently of Experiment 1's usage data. The authors cite their own prior work, Erlei et al. [30], to motivate and interpret the choice-independence construct, but the current experiment uses newly collected data and the conclusion does not reduce to that citation; the self-citation is contextual framing rather than load-bearing evidence. The absence of a same-language-order control and the lack of a reported Language×Order interaction are genuine threats to the causal interpretation, but they concern validity, not circularity: the observed usage drop is not defined to equal the treatment condition. No circular step can be exhibited from the paper's equations or derivation chain.

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

No fitted parameters are used to define the outcomes; experimental design choices (temperature 0.8, incentive amounts, recipe prompts) are hand-set but not tuned to produce the results. The central claim rests on three domain assumptions, listed above.

assumptions (3)
  • domain assumption Utilization differences between first- and second-language tasks are caused by the language of prior exposure, not by task order, fatigue, or learning effects.
    The design has no same-language-order control, so the causal attribution in Result 1 requires this assumption.
  • domain assumption Weighted average similarity between AI-generated segments and final text is a valid measure of reliance on LLM output.
    Used in Section 5.1 to support the reliance claim; no validation against ground-truth authorship.
  • domain assumption Benchmark differences between English and Spanish Llama 3.1 (Multi-IF, PAWS-X, persuasion detection) correspond to a perceptible quality gap in the writing task.
    The manipulation relies on participants experiencing worse Spanish output; this is inferred from benchmarks, not directly measured.

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Cite this review

Pith. "Pith review of Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages." pith.science (2026). https://pith.science/paper/V7JJVIVM

@misc{pith2026250209532,
  author       = {Pith},
  title        = {Pith review of: Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V7JJVIVM}},
  note         = {Machine review of arXiv:2502.09532}
}
read the original abstract

Recent advances in generative AI have precipitated a proliferation of novel writing assistants. These systems typically rely on multilingual large language models (LLMs), providing globalized workers the ability to revise or create diverse forms of content in different languages. However, there is substantial evidence indicating that the performance of multilingual LLMs varies between languages. Users who employ writing assistance for multiple languages are therefore susceptible to disparate output quality. Importantly, recent research has shown that people tend to generalize algorithmic errors across independent tasks, violating the behavioral axiom of choice independence. In this paper, we analyze whether user utilization of novel writing assistants in a charity advertisement writing task is affected by the AI's performance in a second language. Furthermore, we quantify the extent to which these patterns translate into the persuasiveness of generated charity advertisements, as well as the role of peoples' beliefs about LLM utilization in their donation choices. Our results provide evidence that writers who engage with an LLM-based writing assistant violate choice independence, as prior exposure to a Spanish LLM reduces subsequent utilization of an English LLM. While these patterns do not affect the aggregate persuasiveness of the generated advertisements, people's beliefs about the source of an advertisement (human versus AI) do. In particular, Spanish-speaking female participants who believed that they read an AI-generated advertisement strongly adjusted their donation behavior downwards. Furthermore, people are generally not able to adequately differentiate between human-generated and LLM-generated ads. Our work has important implications for the design, development, integration, and adoption of multilingual LLMs as assistive agents -- particularly in writing tasks.

Figures

Figures reproduced from arXiv: 2502.09532 by the authors.

Figure 1
Figure 1. The ABScribe writing interface used in the exper [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Experiment Workflow for LLM-Assisted Writing [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 4
Figure 4. Effect of Initial Language Exposure on AI Drafter Usage by Task Group. Left: Total usage count of the AI drafter feature shows a significant "gap" between task groups based on initial language exposure. The group exposed to English first (ENG_1, followed by ESP_2) shows substantially higher usage compared to the group exposed to Spanish first (ESP_1, followed by ENG_2), as indicated by the significant differ￾ences m… view at source ↗
Figures from the paper (9 more)
Figure 5
Figure 5. Figure 5: Weighted Average Similarity Percentage Across Task Groups and Models. The similarity scores vary across task groups depending on the initial language exposure but pattern remains consistent across embedding models. ENG_1 and ESP_2, which involve starting with English, …
Figure 6
Figure 6. Figure 6: Left: Average donations across treatments. Right: [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: The figure shows the differences in average dona￾tion amounts (left) and donation shares (right) across actual ad sources (Control, Human, Human-AI, and AI). Bars are further categorized by the perceived source of the ad text (AI or Human). Error bars represent the con…
Figure 8
Figure 8. Figure 8: The figure shows the differences in average dona￾tion amounts (left) and donation shares (right) based on par￾ticipant demographics (sex) and language groups (Spanish￾speaking and English-speaking). Bars are further catego￾rized by the perceived source of the persuasiv…
Figure 9
Figure 9. Figure 9: Left: Average word count across treatments, indicat￾ing variations in output length depending on treatment and condition. Right: Average time spent outside the platform across treatments. Error bars represent 95% confidence inter￾vals. 8.3 Stage 1 - Writing Task Metric…
Figure 10
Figure 10. Figure 10: Differences in stated utility and feature usage percentages across treatment groups for English (Left) and Spanish (Right) tasks. Bars represent feature usage percent￾ages, while lines indicate normalized stated preferences as reported in questionnaire responses. This…
Figure 14
Figure 14. Figure 14: Left: Distribution of donation amounts across age groups, showing the variability in donation behavior as a function of age. Right: Comparison of age distributions be￾tween donors and non-donors, highlighting differences in median age and interquartile ranges for both…
Figure 12
Figure 12. Figure 12: Average scores for Behavioral Intention (left), Emo￾tional Appeal (middle), and Information Awareness (right) across various experimental conditions. Error bars represent 95% confidence intervals. 8.6 Stage 2: Donation Behaviour by Demographic Factors Asian Black Mixe…
Figure 13
Figure 13. Figure 13: Left: Average donation amount across different ethnic groups. Right: Share of donors (percentage of partic￾ipants who donated) by ethnicity, highlighting variations in donation behavior and likelihood among demographic groups. Error bars representing 95% confidence in…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.