Gender-Queer Dialect Bias

• Gender-queer dialect bias is a systematic disadvantage where NLP systems misrepresent non-binary language forms and penalize inclusive gender expressions.
• Empirical studies show that large language models default to binary pronouns, overcorrect queer dialects, and amplify negative stereotypes through measurable metrics.
• Mitigation strategies such as counterfactual data augmentation, advanced prompting, and participatory audits offer actionable paths to reduce bias and improve fairness.

Gender-queer dialect bias refers to systematic disadvantages faced by queer and non-binary language users—especially those employing non-binary pronouns, reclaimed lexical items, or gender-inclusive morphologies—when their linguistic practices interact with LLMs and allied NLP systems. This phenomenon encompasses both allocative and representational harms: models not only exhibit degraded accuracy and inappropriate corrections on queer dialect forms, but also tend to erase, pathologize, or otherwise distort non-binary and queer identities. Evidence from recent psycholinguistic, algorithmic, and fairness-driven studies indicates that such bias is deeply embedded in both the architecture and training protocols of state-of-the-art LLMs, with measurable impacts on linguistic generation, moderation, coreference, and creative writing.

1. Typology of Gender-Queer Dialect Bias

Empirical research identifies several axes along which “gender-queer dialect bias” manifests in LLMs and related NLP systems:

• Pronoun and Coreference Bias: LLMs systematically under-prefer non-binary pronouns such as singular “they,” “xe,” “ze,” or gender-inclusive neologisms, often defaulting to traditionally masculine or binary options—even when presented with explicit gender-neutral or inclusive antecedents (Bartl et al., 18 Feb 2025, Lund et al., 2023).
• Erasure and Pathologization: Non-binary, genderqueer, and other “subversive” identity terms display markedly lower generation probabilities than both binary-gendered terms and even random non-human nouns; associations between these identities and mental health conditions are exaggerated, operationalized via metrics such as the Folk–Subversive Log Probability Ratio and Gender–Illness Log Probability Ratio (Hafner et al., 20 May 2025).
• Stereotype Reinforcement: LLMs, when prompted to describe or narrate gender-queer individuals, not only mirror negative human stereotypes (lower ratings for “Warmth” and “Competence”), but also disproportionately generate text centered on adversity and marginalization, thereby amplifying representational harms (Ostrow et al., 10 Jan 2025).
• Content Moderation Over-flagging: Classifiers and moderation algorithms over-flag reclaimed slurs and identity-affirming in-group language used by gender-queer speakers as “toxic,” even after incorporating author identity context and advanced prompting strategies. This results in higher false positive rates and lower F1 scores on gender-queer-authored content (Dorn et al., 23 May 2024).

2. Experimental Methodologies for Quantifying Bias

A rigorous suite of experimental designs—spanning psycholinguistic paradigms, fairness auditing, and Bayesian modeling—has crystallized the extent and mechanisms of gender-queer dialect bias.

• Anaphora Resolution and Coreference Generation: By adapting the two-sentence paradigm of Tibblin et al. (2023), studies presented LLMs with antecedent phrases encoding masculine, feminine, or neutral/gender-fair references, then measured model likelihoods and generated continuations for plural and singular pronouns (“he,” “she,” “they,” “people,” “Männer,” etc.). Statistical tests (two-way ANOVA, χ²) quantified interaction effects of antecedent and coreferent gender, revealing persistent male-default and resistance to gender-neutral forms (Bartl et al., 18 Feb 2025).
• Grammatical Error Correction (GEC) Bias Diagnostics: Counterfactual Data Augmentation (CDA) using linguistically-contrasted pairs (he↔she, singular they, masculine/feminine terms) enabled direct measurement of F_{0.5} score gaps and explicit misgendering rates. Manual annotation of “explicit-bias” instances (e.g., they→he/she conversions) provided a lower bound for representational erasure (Lund et al., 2023).
• Auditing Stereotype Content: Extending the Stereotype Content Model, both humans and LLMs were prompted to rate social groups (women, men, non-binary, lesbian, etc.) along “Warmth” and “Competence.” The models’ numerical ratings, keyword selections, and generated narratives exhibited high correlation with human rank orderings but with amplified negative portrayals for gender-queer groups (Ostrow et al., 10 Jan 2025).
• Bias Metrics: Several formal metrics have been introduced:
  • Folk–Subversive LPR: Alignment with binary/folk gender vs. non-binary/subversive concepts.
  • Sex–Gender LPR: Sensitivity of gender completions to sexed context.
  • Gender–Illness LPR: Differential association of gender-queer identities to illness terms.
  • CRR (Correct Response Rate): For pronoun inclusivity, defined as the fraction of correct judgments per pronoun type (Huang et al., 12 Nov 2024).

3. Empirical Findings Across Tasks and Languages

Pronoun and Coreference Tasks:

• English LLMs overwhelmingly match pronoun gender to antecedent, but with a pronounced masculine default. Following a feminine antecedent, the neutral coreferent is less likely than the masculine one ($e^{-0.236}\approx0.79$ odds ratio, $p<.001$), and after a neutral antecedent, masculine is chosen over feminine by a factor $\approx3.0$ ($e^{1.107}$, $p<.001$). Singular “they” remains systematically dispreferred: $p(\text{they}) < p(\text{he})$ by $\approx8.7\times$ after a neutral antecedent ($e^{2.16}$) (Bartl et al., 18 Feb 2025).
• In German, even advanced models (Mistral-7B) assign the highest likelihood to masculine plurals (“Männer”) in all contexts, with gender-inclusive morphemes only modestly raising the likelihood of “Frauen” or “Personen” by $10$–$20\%$, never surpassing the masculine default (Bartl et al., 18 Feb 2025).

Grammatical Error Correction:

• All evaluated GEC models display a large F_{0.5} drop (–6.2% to –9.5%) on sentences rewritten to use singular “they” compared to binary forms (Lund et al., 2023).
• Targeted augmentation (St-CDA) nearly closes the gap (–1.4%) and reduces explicit misgendering (32→7 errors among 195 test cases), with negligible negative impact on overall system quality.

Content Moderation:

• Off-the-shelf toxicity classifiers (e.g., Detoxify, Perspective API) flag gender-queer in-group uses of reclaimed slurs at high false positive rates (Detoxify: $P=0.15$, $R=0.66$, $F_1=0.25$), vs. significantly higher precision and F_1 for out-group uses (Dorn et al., 23 May 2024).
• LLMs, even with chain-of-thought prompting and explicit identity context, perform poorly on in-group cases ($F_1\leq0.24$ for most models), driven by over-reliance on slur presence ($R^2$ up to 0.32 in vanilla setup).

Stereotype and Representation:

• LLMs reproduce human-like stereotype hierarchies but rate non-binary and bisexual groups significantly lower on competence and warmth (e.g., mean competence ≈2.8 vs. heterosexual ≈3.8). Narrative outputs further typecast queer subjects into adversity-focused genres, reinforcing a “pain narrative” bias rather than diverse, affirming representation (Ostrow et al., 10 Jan 2025).

Pronoun Bias Correction Pipelines:

• Multi-agent collaborative frameworks achieve substantial improvements: a 32.6 percentage point increase in correct opposition to inappropriate binary pronouns in inclusive contexts relative to GPT-4o baseline ($\chi^2=38.57$, $p<0.0001$), with over 94% correct acceptance for non-binary pronouns (Huang et al., 12 Nov 2024).

4. Theoretical Models and Explanations

• Bayesian and Nonparametric Models: The nested Chinese Restaurant Franchise Process (nCRFP) provides a generative account, mapping community-wide, referent-specific, and interaction-level pronoun distributions. Key hyperparameters ($\alpha_0$, $\alpha_1$, $\alpha_2$) control the openness to novel pronouns, individual divergence from group norms, and listener adaptation speed, respectively. This framework enables quantification of “adaptation curves” (pronoun accuracy vs. number of user corrections) and fairness gaps between binary and non-binary forms (Jacobs et al., 3 Apr 2025).
• Performativity-Informed Audit: Grounded in Butler’s gender performativity theory, comprehensive audits reveal that nearly all mainstream LMs (GPT-2, RoBERTa, T5, Llama, Mistral) conflate gender with sex, fail to generate non-binary and gender-diverse terms at rates above random noun baselines, and associate non-binary identities with mental illness (Hafner et al., 20 May 2025). Larger models exacerbate both binary essentialization and erasure of non-binary dialects (Spearman $\rho=0.89$, $p<0.01$ for model size vs. Folk–Subversive LPR).

5. Mitigation Strategies and Evaluation Best Practices

• Data-Centric Solutions: Counterfactual Data Augmentation targeting singular “they” (St-CDA), binary swaps (FM-CDA), and extensions for non-English morphologies demonstrably improve system equity and reduce explicit bias without degrading global task performance. More inclusively curated pretraining corpora—especially authentic gender-diverse narratives, queer community writings, and subversive language forms—are essential for long-run remediation (Lund et al., 2023, Hafner et al., 20 May 2025).
• Model-Centric Approaches: Advanced prompting (chain-of-thought, author-identity context), explicit bias detection pipelines (collaborative agent frameworks), and regularization that minimizes unwanted correlations between gender identity and sex terms all show promise. Integration of community-informed metrics (e.g., group-wise F_1, bias gap, slur-dependence $R^2$, adaptation curves) into continuous evaluation pipelines is recommended (Huang et al., 12 Nov 2024, Dorn et al., 23 May 2024, Jacobs et al., 3 Apr 2025).
• Participatory and Community-Led Audits: Benchmarks and annotation protocols co-designed with queer and non-binary stakeholders enable coverage of pragmatic cues (e.g., reclamation, sarcasm, in-group labels) often misinterpreted by standard tools. Ongoing simulation-based respectfulness audits quantifying misgendering and adaptation speed are advised (Dorn et al., 23 May 2024, Jacobs et al., 3 Apr 2025).

6. Broader Implications and Future Directions

• Structural Erasure: The convergence of binary essentialization, pronoun mismatches, slur over-flagging, and narrative stereotyping creates compounded marginalization for gender-queer dialect users, both in human-AI interaction and downstream applications (content moderation, writing aids, educational tools).
• Theory-Informed Intervention: Addressing gender-queer dialect bias necessitates moving beyond superficial gender association “debiasing” to embrace theoretical insights from gender performativity, intersectionality, and sociolinguistics. This includes explicit recognition of non-binary expressions as fully grammatical, meaningful, and legitimate ways of speaking (Hafner et al., 20 May 2025, Bartl et al., 18 Feb 2025).
• Open Questions: The development of inclusive NLP systems requires (i) scalable augmentation and adaptation to all relevant dialectal features; (ii) holistic evaluation frameworks for narrative, pragmatic, and functional representation; and (iii) participatory, community-centered procedures to track harm, repair, and progress.

In sum, current evidence demonstrates that LLMs and related NLP systems carry entrenched gender-queer dialect biases—manifest in reduced accuracy, representational erasure, pathologization, and overenforcement of binary norms. Robust mitigation will require systemic redesign of data, algorithms, and evaluative methodologies, grounded in both formal statistical metrics and close engagement with queer linguistic communities (Bartl et al., 18 Feb 2025, Hafner et al., 20 May 2025, Dorn et al., 23 May 2024, Lund et al., 2023, Ostrow et al., 10 Jan 2025, Huang et al., 12 Nov 2024, Jacobs et al., 3 Apr 2025).