Large Language Models Discriminate Against Speakers of German Dialects

• URL: http://arxiv.org/abs/2509.13835v1
• Date: Wed, 17 Sep 2025 09:05:37 GMT
• Title: Large Language Models Discriminate Against Speakers of German Dialects
• Authors: Minh Duc Bui, Carolin Holtermann, Valentin Hofmann, Anne Lauscher, Katharina von der Wense,
• Abstract summary: In Germany, more than 40% of the population speaks a regional dialect.<n>We examine whether such stereotypes are mirrored by large language models (LLMs)<n>We find that explicitly labeling linguistic demographics--German dialect speakers--amplifies bias more than implicit cues like dialect usage.
• Score: 44.05620251584259
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Adler and Hansen, 2022). However, despite cultural importance, individuals speaking dialects often face negative societal stereotypes. We examine whether such stereotypes are mirrored by large language models (LLMs). We draw on the sociolinguistic literature on dialect perception to analyze traits commonly associated with dialect speakers. Based on these traits, we assess the dialect naming bias and dialect usage bias expressed by LLMs in two tasks: an association task and a decision task. To assess a model's dialect usage bias, we construct a novel evaluation corpus that pairs sentences from seven regional German dialects (e.g., Alemannic and Bavarian) with their standard German counterparts. We find that: (1) in the association task, all evaluated LLMs exhibit significant dialect naming and dialect usage bias against German dialect speakers, reflected in negative adjective associations; (2) all models reproduce these dialect naming and dialect usage biases in their decision making; and (3) contrary to prior work showing minimal bias with explicit demographic mentions, we find that explicitly labeling linguistic demographics--German dialect speakers--amplifies bias more than implicit cues like dialect usage.

Related papers

• A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation [19.535404632372042]
  Betthupferl is an evaluation dataset containing four hours of read speech in three dialect groups spoken in Southeast Germany.<n>We provide both dialectal and Standard German transcriptions, and analyze the linguistic differences between them.<n>We benchmark several multilingual state-of-the-art ASR models on speech translation into Standard German, and find differences between how much the output resembles the dialectal vs. standardized transcriptions.
  arXiv  Detail & Related papers  (2025-06-03T14:02:52Z)
• Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks [68.33068005789116]
  We introduce ReDial, a benchmark containing 1.2K+ parallel query pairs in Standardized English and AAVE.<n>We evaluate widely used models, including GPT, Claude, Llama, Mistral, and the Phi model families.<n>Our work establishes a systematic and objective framework for analyzing LLM bias in dialectal queries.
  arXiv  Detail & Related papers  (2024-10-14T18:44:23Z)
• Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models [50.40276881893513]
  This study introduces Spoken Stereoset, a dataset specifically designed to evaluate social biases in Speech Large Language Models (SLLMs) By examining how different models respond to speech from diverse demographic groups, we aim to identify these biases. The findings indicate that while most models show minimal bias, some still exhibit slightly stereotypical or anti-stereotypical tendencies.
  arXiv  Detail & Related papers  (2024-08-14T16:55:06Z)
• Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness [16.746758715820324]
  We present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.
  arXiv  Detail & Related papers  (2024-06-14T12:39:39Z)
• What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects [60.8361859783634]
  We survey speakers of dialects and regional languages related to German. We find that respondents are especially in favour of potential NLP tools that work with dialectal input.
  arXiv  Detail & Related papers  (2024-02-19T09:15:28Z)
• Task-Agnostic Low-Rank Adapters for Unseen English Dialects [52.88554155235167]
  Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English. By disentangling dialect-specific and cross-dialectal information, HyperLoRA improves generalization to unseen dialects in a task-agnostic fashion.
  arXiv  Detail & Related papers  (2023-11-02T01:17:29Z)
• Comparing Biases and the Impact of Multilingual Training across Multiple Languages [70.84047257764405]
  We present a bias analysis across Italian, Chinese, English, Hebrew, and Spanish on the downstream sentiment analysis task. We adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for four attributes: race, religion, nationality, and gender. Our results reveal similarities in bias expression such as favoritism of groups that are dominant in each language's culture.
  arXiv  Detail & Related papers  (2023-05-18T18:15:07Z)
• Learning to Recognize Dialect Features [21.277962038423123]
  We introduce the task of dialect feature detection, and present two multitask learning approaches. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features.
  arXiv  Detail & Related papers  (2020-10-23T23:25:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.