United States

Large language models (LLMs) are increasingly used as evaluative tools across languages, yet bias research remains overwhelmingly Anglocentric, with most studies conducted in English using Latin-script names. It remains unclear whether bias patterns generalize across linguistic contexts. We investigate this question and introduce the stereotype perceptual map, a framework for analyzing how ethnic groups are positioned along evaluative dimensions. 
Using 900,000 model responses over 45,000 names spanning 9 ethnicities, we evaluate model behavior across prompt languages (English, Chinese, Thai), writing scripts (Latin, Chinese, Thai), evaluative domains (competence, warmth), and models (GPT, DeepSeek). We find that ethnic bias hierarchies are jointly shaped by local linguistic context and model origin and differ substantially between Western-centric and Sinocentric models. DeepSeek exhibits highly stable rankings across conditions in math competence judgments, consistently placing Chinese at the top, followed by Russian, and White, Hispanic, and Black names at the bottom. GPT, by contrast, shows strong script-dependent reordering: Latin-script conditions form one stable cluster, while native-script conditions form another, with substantially lower cross-cluster correlations. We term this script-gated bias: transliterating the same names into a non-Latin script can activate a different evaluative frame and produce rankings that are sometimes inversely correlated with Latin-script results. Warmth evaluations are less stable than competence across both models.
Our findings demonstrate that multilingual bias cannot be characterized through single-language, single-script audits. For multilingual users, code-switching between languages can toggle between different bias regimes. Fairness evaluations for multilingual LLMs must therefore account for deployment language, writing system, and model origin to capture the full range of potentially harmful stereotypes these systems exhibit.

ACL 2026

Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek

script-gated bias

bias audit

stereotype content model

name-based bias

social stereotypes

ethnic bias

deepseek

names

cross-lingual evaluation

writing systems

ai ethics

stereotypes

ethnicity

race

multilingual nlp

large language models

fairness

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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workshop paper

Bias benchmarks for LLMs largely focus on English, overlooking language- and culture-specific stereotypes. We introduce CrowS-Pairs-NL, a Dutch stereotype benchmark built by filtering, translating, and adapting the English CrowS-Pairs dataset to address known conceptual pitfalls, and extending it with newly crowdsourced Dutch sentence pairs. We evaluate six multilingual and Dutch-trained models using both a pseudo-log-likelihood metric adapted for autoregressive models and a prompt-based metric with three template variants. Models explicitly trained on Dutch data consistently exhibit higher stereotyping scores, suggesting that language-specific fine-tuning introduces language-specific bias. The two metrics broadly agree on model rankings but differ in sensitivity, with the prompt metric showing a narrower range of scores. Our benchmark and findings underscore the need for culturally grounded bias evaluation beyond English.

CrowS-Pairs-NL: A Benchmark to Evaluate Dutch Stereotype Bias in LLMs

Large language models (LLMs) are increasingly used to convert patient language into clinical-style summaries, yet patient symptom descriptions may vary across linguistic, cultural, and cross-linguistic contexts. In this pilot study, we operationalize this variation using four expression styles: direct English, indirect English, culturally mediated English, and Chinese-original patient language. We propose a compact red-teaming framework for testing whether LLM-based symptom interpretation changes when the same underlying concern is expressed in different linguistic and cultural forms. Our pilot dataset contains eight symptom scenarios, each expressed in four styles, yielding 32 vignettes before prompt variation. We evaluate GPT-5 mini as a pilot case-study model under generic and culture-aware prompts, repeating the full evaluation three times to produce 192 model outputs. Reference labels and a stratified subset of model output annotations were reviewed for face validity by an independent reviewer with clinical training.The model usually preserves broad symptom categories, but subtle failure modes emerge. Culture-aware prompting reduces severity downgrades from 14.6% to 9.4% and ambiguity-flagging failures from 28.1% to 13.5%, but does not reduce interpretation inconsistency or clinical category shift, both of which remain at 6.2%. Indirect English shows the highest severity-downgrade and flagging-failure rates, while Chinese-original expressions are often interpreted with the correct broad category but are not consistently flagged as ambiguous. These findings suggest that medical LLM evaluation should assess cultural robustness, severity framing, ambiguity preservation, and human-review escalation in addition to factual accuracy.

Lost in Translation: Cross-Cultural Bias in LLM-Assisted Medical Symptom Interpretation

We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) system than otherwise equivalent neutral queries. We pre-register a four-culture audit covering en-Anglo, es-LATAM, Arabic, and Hindi probes on a synthetic English PII corpus, comparing five paired query arms via the Stereotype-Trigger Leakage Delta (STLD). The locked confirmatory estimator was not run, so all reported tests are exploratory or sensitivity analyses, with deviations documented. We also identify a prompt-echo confound in the name-leakage metric: the model often re-emits the queried name, inflating apparent leakage without retrieval extraction. On cleaner non-name channels—email, phone, SSN-like identifier, and address—we find no stereotype-driven amplification for any culture after multiple-comparison correction. One name-included es-LATAM cell is significant in the negative direction, but matched-arm decomposition and an expanded culture-neutral control sensitivity suggest a high-leak control-predicate sampling artifact rather than a stereotype-treatment effect. Because the study is powered only for mid-sized effects and the culturally marked probe bank mixes stereotype content with cultural markers and heritage practices, we interpret the results as no detection—not evidence of no effect—of culturally marked predicate-triggered PII amplification under this synthetic-English RAG setting. The paper contributes a preregistered stereotype-as-privacy-side-channel test, diagnoses prompt-echo and predicate-resource confounds, and outlines release of the synthetic corpus, predicate bank, query generator, audit scripts, and analysis code upon acceptance

Exploratory As-Analyzed No-Detection of Culturally-Marked Predicate-Triggered PII Amplification in a Synthetic-English RAG Probe: A Predicate-Resource-Confounded Audit

Multilingual large language models exhibit systematic differences in their outputs across languages, even when representing the same underlying knowledge. Prior work has primarily focused on evaluating or reducing such inconsistencies. In this work, we instead study whether cross-lingual behavior can be controlled: specifically, whether answer distributions associated with other languages can be expressed under English prompting. To this end, we construct a human-annotated factual dataset and a cultural scenarios dataset, and compare intervention methods including persona prompting, activation steering, and preference-based fine-tuning. We evaluate how these methods affect answer distributions and their generalization to culturally grounded settings. Our results show that answer distributions can be systematically shifted toward those observed in other languages, with persona prompting consistently outperforming more complex intervention methods.

Controlling Cross-Lingual Answer Distributions in Language Models: Enabling Transfer of Factual Preferences

Large language models (LLMs) are increasingly explored for patient-facing medical advice and symptom triage, yet their responses may shift when identical clinical evidence is paired with culturally marked patient descriptors. 
We present a counterfactual audit framework for evaluating cross-cultural variation in LLM-generated medical advice by isolating identity-related cues while holding clinical evidence constant.
Our evaluation uses matched clinical vignettes, cross-regional and culturally marked prompt variants, repeated sampling, and structured comparison of urgency framing, safety recommendations, empathy, and escalation advice.
Across multiple commercial and open-weight LLMs, we observe measurable identity-conditioned variation in both triage decisions and interactional framing. In several cases, culturally marked descriptors shift urgency assessments or escalation recommendations despite unchanged clinical evidence. While the magnitude and direction of these effects differ across models, the results suggest that LLM-generated medical advice remains sensitive to culturally linked identity cues in ways that may affect safety-critical guidance.
Our results demonstrate how culturally grounded counterfactual auditing can help identify clinically unsupported variation while distinguishing potentially harmful shifts from appropriate communication adaptation in patient-facing medical advice.

Counterfactual Auditing of Cross-Cultural Variation in LLM-Generated Medical Advice

Large language models (LLMs) perpetuate cultural stereotypes not only through biased associations but through systematic omission and orthographic erasure of underrepresented languages. We present empirical evidence of two compounding failure modes affecting Northeast Indian languages: (1) entity-level invisibility, where state-of-the-art NER systems score F1=0.000 on culturally critical named entities such as Khasi surnames, Garo festivals, and tribal names; and (2) orthographic corruption, where LLM tokenizers corrupt semantically meaningful diacritics (ï, ñ) and the Garo morpheme boundary marker (U+00B7) at rates of 18.8–50% across four of five evaluated models. Drawing on NortheastNER (F1=0.964, six entity categories, XLM-RoBERTa-base) and a systematic tokenization study across Khasi and Garo, we argue that stereotype-by-omission constitutes a distinct and measurable harm to indigenous language communities. We further show that a custom multilingual tokenizer achieves 26–50% token reduction over five baseline LLMs, demonstrating that culturally grounded infrastructure can partially remediate these failures. Our findings call for cultural representation audits as a standard component of multilingual NLP evaluation.

Stereotyped by Silence: How LLMs Erase Northeast Indian Languages Through Omission and Orthographic Corruption

Stereotypes associate a group (G) with an attribute (A), often through broad generalizations like "Gs are A". In the age of large language models, many existing datasets and methods measure bias by considering changes in $P(A|G)$ between different social groups ($G_{alt}$). Motivated by literature on generics, we argue that constructing minimal pairs along the group dimension is not enough: explicitly considering the alternate attribute ($A_{alt}$) is necessary for a robust score. We first show how to adapt existing log-probability metrics to this setup, but we find that aggregating them across different stereotypes is inherently flawed. We propose a new metric representing the mutual information (MI) between the social groups and attributes. We argue that it is better suited for aggregation across individual points, allowing for robust comparison of stereotype strength across different languages and models. We demonstrate this on a set of paired English and Russian stereotypes from the BiasShades dataset.

The Missing Minimal Pair: Mutual Information for Stereotype Evaluation in LLMs

Real-world social judgments often depend on local norms, values, and situational expectations. We study whether multilingual language models use such cultural context robustly, or whether their judgments are overly sensitive to surface cues such as prompt language, country labels, and externally supplied contextual descriptions. Building on NormAd, a benchmark for cultural norm adherence, we keep the underlying scenario fixed while perturbing language, context granularity, and contextual consistency. We evaluate 12 models from four model families: Cohere Command, Cohere Tiny-Aya, Alibaba Qwen, and Google Gemma. Across three countries, five language conditions, and 55,000 raw generations, models benefit most from explicit norm descriptions. Adding these descriptions improves accuracy by 17.8 percentage points over country-only prompts. In contrast, adding externally supplied rules that are not applicable to the scenario is the largest harmful perturbation, reducing accuracy by 24.5 points. Language changes produce 9–14% prediction flips, but these flips are directionally balanced. These results show why flip rate alone is insufficient: it does not distinguish between correct-to-wrong (C→W) and wrong-to-correct (W→C) transitions. We therefore report cultural robustness using signed accuracy change, C→W/W→C decompositions, and perturbation-specific vulnerability alongside clean accuracy.

Robustness of Cultural Norm Reasoning Under Language and Context Perturbations

As language models scaled to internet data, exhaustive curation became infeasible and stereotype learning became an expected side effect of training. A common response is post-training local editing: identify an internal direction linked to harmful behavior and try to remove it. However, the causal effectiveness of this method is unclear. In this paper, we present a causal audit of local stereotype editing across seven open-weight LLMs using matched inject-versus-remove interventions, component-level diagnostics, and cross-source transfer stress tests. The main result is asymmetric controllability: stereotype preference is easier to increase than to reliably decrease. In matched tests, score-level inject-minus-remove asymmetry is significant in 5/7 models, and margin-level asymmetry is significant in 7/7. Supporting diagnostics show model-dependent sign reliability, suppressor contamination in ranked intervention sets, and no reliable large-effect cross-source transfer under current settings. Overall, localization provides control leverage but not a debiasing guarantee; deployment-facing claims should be validated with direct causal intervention and transfer stress tests.

Easy to Add, Hard to Erase: Causal Evidence on Stereotype Editing in LLMs

MLLM-as-a-Judge is conventionally validated by agreement with human annotations, but agreement against a culturally heterogeneous pool is pool-dependent: it silently conditions on whose judgments the aggregate represents. We introduce VOIR DIRE, a multimodal benchmark of 626 culturally paired image–prompt items spanning U.S. and mainland Chinese contexts across food, fashion, and architecture, with annotator pools that are within-pool reliable (α = 0.86/0.74) but cross-pool divergent on evaluation (Q1 r = −0.12). Across six MLLMs, the bias decomposes into two failures: a positivity-floor calibration failure (compressed scale use) and an orientation failure (default to one cultural norm). On this corpus, where contested items are sampled to split the two pools, the floor mechanically validates the more permissive Chinese reading; persona prompting partially recovers calibration, but the orientation residual survives, evidence the tilt is not reducible to scale compression. Reference pool in-context demonstrations deepen the orientation residual and inflate the high end rather than restoring use of the low end. Model origin adds a small additive tilt (~ 0.10 MAE) that is approximately invariant under demonstration. We recommend reporting alignment against each reference pool separately and treating cross-pool divergence as a judge property.

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CrowS-Pairs-NL: A Benchmark to Evaluate Dutch Stereotype Bias in LLMs

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Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek

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