Dialect Naming Bias in NLP

• Dialect naming bias is the adverse stereotyping triggered by explicit dialect labels, resulting in underrepresentation and unfavorable outcomes in computational models.
• Empirical studies show that NLP tasks, including text summarization and LLM evaluation, systematically misrepresent dialectal varieties such as African American English.
• Mitigation involves participatory design, debiasing techniques, and rethinking labeling practices to improve fairness and representational accuracy in language technologies.

Dialect naming bias refers to the negative stereotyping, underrepresentation, or allocation of less favorable outcomes by computational systems and research methodologies toward speakers or texts explicitly designated or treated as “dialectal,” particularly when those designations invoke social or linguistic category labels (e.g., “dialect speaker,” “African American English,” “Chicano English”). This phenomenon has been empirically documented across a range of NLP tasks, including text summarization, reasoning, classification, LLM evaluation, and speech recognition. Dialect naming bias is distinct from general dialect bias in that it is specifically triggered or amplified by the explicit act of categorizing or flagging language use with a dialect label, as opposed to implicit cues in language alone.

1. Conceptualization, Definition, and Sociolinguistic Foundations

Dialect naming bias is grounded in sociolinguistic research documenting that societal stereotypes are strongly associated with explicit dialect labels or markers. Experimental and corpus studies find that associating a speaker with a named dialect (e.g., “Writes in Alemannic German dialect”) primes negative attributions on traits such as conscientiousness, education, and urbanity, as opposed to when only dialectal linguistic features are present without explicit labels. This effect has been rigorously quantified for German dialects (Bui et al., 17 Sep 2025), English varieties, and other sociolinguistically salient language variants.

The bias is operationally separable into:

• Dialect Naming Bias: Negative or stereotyped outcomes resulting from the explicit presence of a dialect label.
• Dialect Usage Bias: Bias resulting from the presence of dialectal linguistic features in the text, absent any explicit label.

Empirical results show that explicitly naming a linguistic demographic amplifies bias relative to only implicit dialectal cues (Bui et al., 17 Sep 2025). This is in contrast to some work on race-related labels, where overt demographic mentions may reduce certain types of model bias.

2. Empirical Manifestations in Language Technologies

Dialect naming bias manifests in a variety of language technologies. In text summarization, standard extractive methods (TF-IDF, TextRank, Word2Vec-based centroid, SummaRuNNer) consistently under-represent minority dialects (e.g., African American English) in generated summaries, even when the original dataset contains a proportional fraction of such content (Keswani et al., 2020). For example, in the TwitterAAE dataset, summaries generated by these algorithms included a systematically lower fraction of AAE tweets than in the original, highlighting systemic underrepresentation.

In LLM evaluation, explicit dialect labels cause models to associate dialect users with negative traits in both association and decision tasks. For German dialects, LLMs systematically linked dialect speakers to negative adjectives such as “careless,” “closed-minded,” and “uneducated,” and assigned them to lower prestige occupations or rural regions, reversing even supposedly positive stereotypes (e.g., “friendly”) (Bui et al., 17 Sep 2025). These effects are quantifiable: the bias score for dialect-named conditions is often significantly greater than for usage-based cues alone, with large (statistically significant) deviations from a zero-bias baseline.

In reasoning, language understanding, and human-computer interaction scenarios, enforced or implied dialect labels exacerbate disparities in system performance and user experience, as seen in the comparative accuracy gaps and increases in quality-of-service harms reported for both English and non-English dialects (Lin et al., 14 Oct 2024, Fleisig et al., 13 Jun 2024, Gupta et al., 25 Feb 2025, Harvey et al., 4 Jun 2025).

3. Mechanisms and Methodological Approaches

The mechanisms underlying dialect naming bias are multifaceted. Among the critical drivers are:

• Model Training Distributions: LLMs and downstream classifiers are overwhelmingly trained on standard or majority language varieties, with minimal exposure to named dialect categories (Lin et al., 14 Oct 2024, Fleisig et al., 13 Jun 2024). When exposed to explicit dialect naming, models default to entrenched cultural stereotypes.
• Prompt Structure and Label Amplification: Experimental paradigms involving explicit dialect labels (as in the association and decision tasks) amplify negative bias relative to implicit dialectal usage. This is in contrast to findings in some demographic bias research where explicit mention can reduce bias; for dialects, the amplifier effect is robust (Bui et al., 17 Sep 2025).
• Evaluation Corpus Design: Parallel corpora—constructed with both dialectal and standard variants of the same content—enable the systematic isolation of naming bias effects. For German, the WikiDIR dataset is leveraged to create 350 dialect-standard pairs spanning seven dialects (Bui et al., 17 Sep 2025).

Table: Comparison of Bias Types

Bias Type	Trigger	Effect Magnitude
Dialect Naming Bias	Explicit label in prompt	Highest
Dialect Usage Bias	Only implicit dialectal cues	Substantial, but lower

4. Impact Across Tasks and Modalities

The presence of dialect naming bias has direct consequences across NLP and speech technologies:

• Fairness and Representation: Explicit identification as a dialect speaker leads systems to allocate less favorable outcomes or ratings, often aligning with negative cultural stereotypes (Bui et al., 17 Sep 2025, Fleisig et al., 13 Jun 2024).
• Quality-of-Service (QoS) Harms: In dialogue, summarization, and information services, named dialect users receive lower-quality responses, diminished model comprehension, increased unsureness, and factual errors (Harvey et al., 4 Jun 2025). These issues are exacerbated for minoritized dialects and in the presence of non-standard orthography or typos.
• Amplification Through Labeling: Explicit demographic labeling can amplify bias even above the levels observed in usage-based or context-only scenarios—contrary to some precedent in demographic research (Bui et al., 17 Sep 2025).
• Domain-Specific Disparities: Bias is further pronounced in tasks requiring social, cultural, or context-specific knowledge, such as reasoning in the social sciences and humanities, where accuracy drops are especially acute for dialectally labeled inputs (Zhou et al., 6 Mar 2025, Lin et al., 14 Oct 2024).

5. Mitigation Strategies and Frameworks

Recent work proposes a range of mitigation strategies to address dialect naming bias:

• Feature-Based and Continuum Approaches: Encouraging models to recognize linguistic variation as a continuum of features rather than discrete, labeled categories can reduce reliance on naming and avoid rigid, biased partitions (Demszky et al., 2020, Liu et al., 2023, Shim et al., 18 Oct 2024).
• Participatory and Community-Involved Design: Engaging speakers of minoritized dialects not only in dataset creation but also in participatory evaluation and control set curation (as in the VALUE and summarization frameworks (Ziems et al., 2022, Keswani et al., 2020)) has demonstrated improved outcomes for representational equity and model utility.
• Debiasing and Reweighting in Model Training: Rebalancing training data, auxiliary dialect classification in multitask frameworks, and explicit inclusion of dialectal features during model adaptation or summarization can close the representation gaps (Spliethöver et al., 14 Jun 2024, Liu et al., 2023, Keswani et al., 2020).
• Cautious Use of Demographic Labels: Given that explicit labeling amplifies bias, models should avoid unnecessary or default recourse to dialect naming labels in system outputs, prompts, or decision explanations (Bui et al., 17 Sep 2025).
• Audit and Quality-of-Service Frameworks: Chatbot auditing frameworks using dynamically generated dialect-converted prompts and measuring direct QoS harms allow for practical, large-scale assessment by both internal and external parties (Harvey et al., 4 Jun 2025).

6. Broader Significance and Future Directions

Dialect naming bias has substantial implications for linguistic justice, technological inclusivity, and representational fairness. Model and dataset design choices around labeling, granularity of categorization, and participatory evaluation materially affect the outcomes for speakers of non-standard, minoritized, or regionally marked varieties. Evidence suggests that as models grow in size and complexity, naming bias not only persists but may strengthen if not explicitly addressed (Bui et al., 17 Sep 2025).

Suggested directions for future work include:

• Further investigation into the mechanisms by which explicit naming amplifies bias (including possible reinforcement during both pretraining and instruction following).
• Extending mitigation approaches to a broader array of languages and dialect continua beyond English and German, with increased focus on within-category variation (Shim et al., 18 Oct 2024).
• Advancing dialect feature detection and leveraging fine-grained dialectometry to inform both model development and evaluation protocols (Demszky et al., 2020, Shim et al., 18 Oct 2024).
• Embedding debiasing protocols and participatory design at all stages of language technology deployment, combining both structural and sociolinguistic expertise.

In summary, dialect naming bias is a rigorously documented phenomenon in computational linguistics, especially pronounced when linguistic demographics are explicitly named. Its mitigation demands both technical and participatory interventions, as well as a principled move away from over-reliance on categorical dialect labels in data, evaluation, and algorithmic practice.