{"id":"49ef2c77-97d9-4019-9fca-2b2b19bc3605","arxiv_id":"2412.00962","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"Across five LLMs and two global surveys, model-generated moral judgments poorly matched cross-cultural patterns of agreement and disagreement.","lead":"This study tested five language models to see whether they mirror how people in different countries agree or disagree on moral questions like divorce and homosexuality. The models mostly failed, showing weak or negative agreement with survey data and often treating controversial topics as universally acceptable.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"WVS preprocessing in §4.1 replaces missing/not-asked codes with 0 on a 1–10 scale, biasing ground-truth country scores and undermining all three evaluation methods.","rationale":"The reader's weakest_assumption is the English-prompt/country-name validity concern in §4.3. That is real but partially acknowledged in the paper's Limitations (prompt sensitivity) and is not strictly an error: the study explicitly tests models' ability to answer such prompts. The preprocessing issue is more load-bearing because it corrupts the ground truth that all three methods use as the reference. It is a concrete, verifiable data-handling mistake, not just a construct-validity debate. The paper's central claim is a negative result about model performance; if the empirical WVS scores against which models are judged are biased by the 0-replacement, the negative result may be an artifact. The concrete test would settle this. If the rerun yields the same pattern, the concern is moot and the conditional acceptance stands. If not, the paper's strongest claim would need qualification. I therefore keep the reader's CONDITIONAL verdict: revision should require fixing the preprocessing and rerunning analyses.","tokens_in":19178,"tokens_out":4820,"duration_ms":43962,"concrete_test":"Recompute WVS country-topic moral scores by excluding codes -1,-2,-4,-5 as missing (drop rows; do not impute 0), then rerun the full pipeline: variance correlations (Tables 1, 5), cluster alignment CAS (Tables 11–16), and direct-probe confusion matrices/χ² (Tables 17–20) for all five models. If the qualitative pattern—weak/negative variance correlations, near-zero CAS, ~0.5 accuracy—persists unchanged, the central claim survives; if any headline result shifts (e.g., a correlation becomes significant or a model's CAS moves from ≈0 to >0.1), the conclusion that models fail to reflect WVS moral divergence is unsupported as stated.","verdict_should_be":"UNCHANGED","load_bearing_attack":"In §4.1, responses coded -1,-2,-4,-5 ('Don't know', 'No answer', 'Not asked in survey', 'Missing') are replaced with 0 before averaging. WVS ethical-items responses are on a 1–10 justifiability scale, so 0 is not a neutral placeholder: it sits below the valid range and drags country means downward. Worse, 'Not asked in survey' means the item was not administered in that country; converting it to 0 fabricates an extreme 'never justifiable' score for entire country-topic pairs. This inflates cross-country variance, distorts the 'most controversial/agreed' topic rankings (Tables 3–4, 7–8), and corrupts the clusterings used as ground truth in Methods 1–3. The paper's claim that this replacement 'ensures that non-responses do not influence the computed averages' is false. Section 3.1 describes WVS Wave 7, while §4.1 says 'version 5' and uses Q177–Q195, adding uncertainty about which wave was actually used. Since every method benchmarks model scores against these survey-derived scores, the central negative finding is not reliably established until the ground truth is recomputed with missing codes excluded rather than set to 0.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper asks whether current LLMs reflect cross-cultural moral divergence and agreement, using three methods: comparing variance in moral scores, comparing country clusterings, and probing with direct comparative prompts. The ground truth comes from the WVS Wave 7 and PEW 2013 surveys. Across GPT-2 variants, OPT, Qwen, and BLOOM-family models, the paper reports weak or negative correlations with survey variances, low cluster alignment, and near-chance performance in direct probing, concluding that the models reflect a homogenized, rather liberal, W.E.I.R.D.-leaning view of moral values. The paper is transparent about limitations and does not fit model parameters to the survey data, so the evaluation is external rather than circular.","tokens_in":19526,"tokens_out":5530,"duration_ms":49517,"significance":"If the finding is reliable, it is practically important: it would indicate that current open-weight LLMs do not yet encode empirically observed cross-cultural moral variation, with implications for fairness and global deployment. The paper also makes a useful methodological contribution by combining three complementary probing strategies and two independent survey datasets. Its credibility, however, depends on the integrity of the survey ground truth and on the exact identity of the models tested. The most serious issue is the WVS preprocessing, which in its current form biases the ground-truth scores and therefore undermines the central negative claim. Because that error is fixable and the manuscript is otherwise transparent, the work merits major revision rather than rejection.","major_comments":[{"comment":"The preprocessing step that replaces WVS response codes -1, -2, -4, and -5 ('Don't know', 'No answer', 'Not asked in survey', 'Missing') with 0 is invalid for the 1–10 justifiability scale. 0 lies below the valid range, so country means are pulled downward; for 'Not asked in survey' the replacement fabricates an extreme 'never justifiable' score for entire country–topic pairs, which inflates cross-country variance and distorts the controversial/agreed topic rankings in Tables 3–4 and 7–8 and the ground-truth clusterings used in Methods 1–3. The statement that replacement with 0 'ensures that non-responses do not influence the computed averages' (Section 4.1) is therefore false. The central negative finding is not reliable until the survey scores are recomputed with these codes excluded or otherwise properly handled.","section":"§4.1"},{"comment":"The methods section states that OPT-350M and BLOOMZ-560M were used (Sections 4.2.1–4.2.2), but every results table lists 'OPT-125' and 'BLOOM' (e.g., Tables 1–2, 11–20). This discrepancy makes it impossible to know which models were actually evaluated and obscures the model-size and multilinguality comparisons that the discussion relies on. Please identify the exact Hugging Face model identifiers and use matching names throughout.","section":"§4.2 and all result tables"},{"comment":"The model-generated country moral scores assume that English prompts of the form 'In {country} {topic} is {moral_judgment}' yield a measure of that country's moral stance. Given that all tested models are predominantly English-trained, the country name may trigger stereotypes or Western-normative associations rather than empirically observed cultural norms. The paper does not validate this scoring method (e.g., against local-language probing or an auxiliary task). Because all three evaluation methods benchmark model scores against survey scores, this is a load-bearing validity assumption. Please discuss this limitation explicitly and, ideally, provide a concrete sensitivity check.","section":"§4.3"}],"minor_comments":[{"comment":"The statement that the PEW 2013 survey has '100 participants from each of the 39 countries' appears inconsistent with the typical Pew Global Attitudes sampling of about 1,000 respondents per country; please verify and correct if needed.","section":"§3.2"},{"comment":"The claim that 'The survey questions were given in English' should be clarified, since the Pew Global Attitudes survey is normally administered in local languages.","section":"§3.2"},{"comment":"Section 3.1 describes the data as WVS Wave 7, while Section 4.1 refers to 'version 5 of the World Values Survey (WVS) data'; please clarify whether these refer to the same dataset release.","section":"§3.1 and §4.1"},{"comment":"The text reports a p-value of 0.014 for BLOOM on the PEW data, but Table 20 lists 0.032; also the surrounding text says 'WVS scores' when the table is for the PEW dataset.","section":"§5.3 and Table 20"},{"comment":"Table 37 is headed 'Top 5 most agreed on PEW topics according to GPT-2 Large' but lists only three topics.","section":"Appendix, Table 37"},{"comment":"The text says 'the same two topics ... sex before marriage and homosexuality' are most controversial in both datasets, but the PEW item is 'sex between unmarried adults'; the wording should be aligned with the actual item labels.","section":"§5.1"},{"comment":"The Pearson correlations and chi-square tests are conducted over multiple models and two datasets without multiple-comparison correction; the reported p-values should therefore be interpreted as exploratory, and this should be stated.","section":"§5.1 and §5.3"}],"recommendation":"major_revision","confidential_remarks":"The manuscript has a serious but fixable data-processing error in the WVS ground truth, and the model-identity mismatch must be resolved before the findings can be trusted. I see no circularity problem: no model parameters are fitted to the survey data, and reusing the same K for model clustering is a defensible design choice. The paper would be suitable for the journal after the requested revisions, but the authors should be asked to recompute all analyses with corrected survey scores and to confirm whether the qualitative conclusions survive."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the negative result is plausible and consistent with prior work, but the WVS ground-truth preprocessing error in Section 4.1 makes the central claim unreliable until it is fixed.\n\nWhat's actually new: the cluster-alignment (ARI/AMI) and direct comparative probing evaluations across GPT-2, OPT, Qwen, and BLOOM are not present in the papers they cite. The variance method is borrowed from Ramezani and Xu, but the model set and the two-survey comparison add something. The prompt design is reasonable: multiple token pairs, two prompt styles, log-probability scoring, and averaging over token pairs. The limitations section is honest about averaging, prompt sensitivity, and random cluster sampling.\n\nWhere it goes wrong: in Section 4.1 they replace WVS codes -1 (don't know), -2 (no answer), -4 (not asked in survey), and -5 (missing) with 0 before averaging. The ethical-items responses are on a 1-10 justifiability scale, so 0 is not a neutral placeholder; it sits below the valid range and drags country means downward. Worse, -4 means the item was not administered in that country, so converting it to 0 fabricates an extreme 'never justifiable' score for entire country-topic pairs. That inflates cross-country variance, distorts the 'most controversial/agreed' topic rankings (Tables 3-4, 7-8), and corrupts the clusterings used as ground truth in all three methods. The paper's claim that this replacement 'ensures that non-responses do not influence the computed averages' is false. Every benchmark compares model scores against these survey-derived scores, so the central negative finding is not reliably established until missing codes are excluded rather than set to 0.\n\nAlso in need of cleanup: Section 4.2 describes OPT-350M and BLOOMZ-560M, but the results report OPT-125 and BLOOM. That mismatch matters because BLOOMZ is an instruction-tuned variant of BLOOM, not the same model. And in Section 5.1 the text says GPT-2 Medium and BLOOM show moderate-to-strong positive PEW correlations, but Table 5 lists GPT-2 Medium at -0.090 and GPT-2 Large at 0.617. The text should say GPT-2 Large and BLOOM.\n\nNone of these are fatal to the research question. The broad negative conclusion matches Arora et al. and Benkler et al., so this is a replication with new evaluation angles. But those angles are built on a corrupted ground-truth benchmark, so this version does not establish its contribution.\n\nVerdict: it deserves a serious referee, but only after the WVS preprocessing is redone and the model identities are pinned down. Send it to review with a clear request for revision rather than desk-rejecting. If the corrected ground truth still shows low alignment, the paper becomes a useful cross-model replication with a cleaner evaluation design.","headline":"Plausible negative result, but the WVS ground-truth preprocessing error makes the central claim unreliable until it is fixed.","tokens_in":19997,"tokens_out":3646,"would_cite":false,"duration_ms":30052,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Six open LLMs probed here mostly fail to mirror cross-cultural moral disagreement, instead projecting a homogenized, Western-leaning moral outlook.","keywords":["large language models","cross-cultural morality","moral values","World Values Survey","cultural bias","W.E.I.R.D. bias","prompt-based probing","moral value pluralism"],"falsifier":"Run the identical probe suite on a model pretrained predominantly on non-Western, non-English text (e.g., a monolingual Arabic or Hindi model) and check whether the variance correlations, cluster alignments, and probe accuracies against WVS/PEW data exceed the near-zero and chance-level values reported here; if they do, the homogenization is a training-data artifact rather than an inherent LLM limitation. A second test is to re-run the probes with country names replaced by survey-derived cues (e.g., 'in a country where most people say divorce is never justifiable') and see whether the models' moral scores then track the survey variances; if they do, it would indicate the models possess the relevant moral knowledge but the country-prompt format does not retrieve it.","tokens_in":19033,"feed_emoji":"🌍","tokens_out":11858,"duration_ms":94734,"temperature":0.7,"pith_summary":"This paper asks whether large language models mirror how moral judgments actually differ and agree across countries. Using the World Values Survey Wave 7 and the Pew 2013 Global Attitudes survey as ground truth, the authors probe six open transformer models with templated moral statements and compare the model-generated scores with the survey scores in three ways: cross-topic variance, country clustering, and direct comparative judgments. All three comparisons show weak to near-chance alignment, and the models systematically compress cultural disagreement—they assign higher average moral acceptability and lower variance than the surveys, sometimes listing the most controversial topics (sex before marriage, homosexuality) as among the most agreed upon. The paper concludes that the tested models propagate a homogenized, autonomy-endorsing view consistent with W.E.I.R.D. (Western, Educated, Industrialized, Rich, Democratic) societies, with no convincing advantage for multilingual models or larger parameter counts. A sympathetic reader would care because LLM outputs increasingly mediate global services and public discourse; if the claim is right, these systems systematically under-represent moral pluralism.","feed_headline":"LLMs flatten moral disagreement across cultures, study finds","feed_subtitle":"If a model tells you what the world thinks, you're mostly hearing Western training data.","key_machinery":"The load-bearing object is the log-probability moral score. For every country-topic pair, the model is prompted to complete 'In {country} {topic} is {moral_judgment}' and 'People in {country} believe {topic} is {moral_judgment}' with five contrasting token pairs—always justifiable/never justifiable, right/wrong, morally good/morally bad, ethically right/ethically wrong, ethical/unethical. The score for a pair is the log probability of the moral token minus the log probability of the immoral token, averaged across the five pairs and both prompt styles, giving one model-generated moral score per country-topic pair that is meant to be comparable to the survey mean. This score feeds all three analyses: Pearson correlation of topic variances, K-means country clustering evaluated by adjusted rand index and adjusted mutual information, and direct comparative prompts that ask whether two countries' judgments are similar or dissimilar. The entire argument depends on these scores being a valid proxy for cultural moral stance.","core_discovery":"The central claim of the paper, stated in the abstract and conclusion, is that the language models tested show 'overall variable and low performance in reflecting cross-cultural differences and similarities in moral values.' Concretely, the correlation between model-generated and survey-based cross-country moral-score variances is weak and mostly negative for the WVS (e.g., r = -0.195 for GPT-2 Medium, r = -0.200 for Qwen) and only moderately positive for the PEW data for GPT-2 Large and BLOOM, without reaching statistical significance. Country clusterings derived from model scores align poorly with survey-derived clusterings, with adjusted rand indices near zero or slightly positive (best Combined Alignment Score 0.215 for Qwen on all WVS topics). Direct comparative probing yields accuracies around 0.5, at or below chance, with some models simply predicting the same class throughout. The paper further claims that the models show a homogenized view: they rate most moral topics as more acceptable and less variable across countries than the surveys do, and they 'generally seem to reflect a rather liberal view, in line with the autonomy-endorsing values found in W.E.I.R.D. societies.' The authors conclude that neither multilinguality nor model size within the tested families convincingly improves this cultural calibration.","pith_inferences":["Beyond the paper's claims: the country-prompt design may itself elicit stereotyped associations rather than moral norms, so a natural extension is to vary the prompt language—presenting the same items in the country's dominant language or with demographic context (age, education, urban/rural)—to test whether model-country scores move closer to survey scores when the measurement frame is less Engli","Beyond the paper's claims: the systematic variance compression (higher mean, lower variance) resembles a default to the most frequent training patterns; a directly testable version of this hypothesis is to measure output perplexity or lexical diversity for low-resource countries—if the model's completions for those countries are more generic or higher-perplexity, the compression would track traini","Beyond the paper's claims: because the study compares country means, it cannot speak to within-country divides; probing with demographic modifiers (gender, age, education) could reveal whether models reproduce the demographic gradients found in WVS data, providing a more sensitive test of cultural understanding than country means alone.","Beyond the paper's claims: the results do not rule out that alignment techniques (instruction tuning, RLHF, or constitution-style training) could improve cultural calibration; a useful next experiment is to fine-tune an open model on survey-style moral judgments with explicit country context and measure whether the probing scores improve."],"forward_implications":["If the finding holds, LLMs deployed in global settings will systematically understate how much countries disagree on moral issues, flattening cultural diversity in automated outputs such as search, recommendation, and decision-support systems.","The near-chance performance on direct comparative probes implies that even when explicitly asked whether two countries' moral judgments are similar or different, these models do not reproduce survey-observed cultural distances.","The models' failure to identify sex before marriage and homosexuality as the most controversial topics suggests particular blind spots on issues where cultural values diverge sharply.","The absence of a convincing multilingual or size advantage in these experiments undercuts the common assumption that simply scaling or diversifying pretraining data fixes cultural bias.","The results argue for evaluation benchmarks built on ground-truth cross-cultural surveys, and for careful auditing of LLMs before they are used to represent 'what people believe' in a global context."],"supporting_citations":[{"why":"Provides the World Values Survey Wave 7 dataset used as the primary ground truth for cross-national moral scores.","marker":"(Haerpfer et al., 2022)"},{"why":"Supplies the probing approach for extracting cultural values from pretrained language models and prior evidence that LLMs may encode such values.","marker":"(Arora et al., 2022)"},{"why":"Introduces the variance-comparison method that this paper adapts in its first analysis, and offers a contrasting positive result the negative findings engage with.","marker":"(Ramezani and Xu, 2023)"},{"why":"Provides the claim that LLMs exhibit a W.E.I.R.D. moral bias, which this paper uses to interpret the homogenized model outputs.","marker":"(Benkler et al., 2023)"},{"why":"Supplies the autonomy-versus-community moral framework and the W.E.I.R.D.-vs-non-W.E.I.R.D. distinction used to characterize the models' liberal lean.","marker":"(Graham et al., 2016)"}],"fun_headline_variants":["LLMs fail to mirror cultural moral differences","Study: LLMs show Western bias in moral views","LLMs flatten moral disagreement across societies","LLMs poorly reflect cross-cultural moral codes","LLMs skew toward WEIRD moral values, study says"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The validity of the model-generated moral scores rests on the assumption that asking an English-prompted LLM 'In {country} {topic} is {moral_judgment}' yields a measure of that country's moral stance, rather than a reflection of the model's English-centric training and stereotypes.","fun_headline_variants_meta":{"raw":{"variants":["LLMs fail to mirror cultural moral differences","Study: LLMs show Western bias in moral views","LLMs flatten moral disagreement across societies","LLMs poorly reflect cross-cultural moral codes","LLMs skew toward WEIRD moral values, study says"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000231,"raw_usage":{"total_tokens":1535,"prompt_tokens":1047,"completion_tokens":488,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":663,"completion_tokens_details":{"reasoning_tokens":418}},"tokens_in":663,"tokens_out":488,"duration_ms":5035,"temperature":1.0,"reasoning_tokens":418,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T04:48:57.567720+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the identical probe suite on a model pretrained predominantly on non-Western, non-English text (e.g., a monolingual Arabic or Hindi model) and check whether the variance correlations, cluster alignments, and probe accuracies against WVS/PEW data exceed the near-zero and chance-level values reported here; if they do, the homogenization is a training-data artifact rather than an inherent LLM limitation. A second test is to re-run the probes with country names replaced by survey-derived cues (e.g., 'in a country where most people say divorce is never justifiable') and see whether the models' moral scores then track the survey variances; if they do, it would indicate the models possess the relevant moral knowledge but the country-prompt format does not retrieve it.","supporting_citations":[{"cited_title":"Johnson, and Liane Zhang","cited_arxiv_id":null,"evidence_quote":"Supplies the autonomy-versus-community moral framework and the W.E.I.R.D.-vs-non-W.E.I.R.D. distinction used to characterize the models' liberal lean."}],"review_version":1}