Dialect Bias in Language Models

Latest papers

16 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Answering in the User's Variety: Calibrated Accommodation Pairs for Post-Training
Oct 20, 2026Zhizhuo Kou, Heming Li, Jian Yang +4Dialect Bias in Language Models Language Model Post-Training
1The Hong Kong University of Science and Technology · 2The Hong Kong University of Science and Technology (Guangzhou) · Peking University
Does Modern Standard Arabic (MSA) Dominate Arabic Dialects in LLMs? A Representation-Level Analysis
Oct 8, 2026Abdu Sallouh, Nicholas Popovič, Michael FärberArabic Language Models Arabic NLP
Technical University of Dresden · Mohamed bin Zayed University of Artificial Intelligence · ScaDS.AI, TU Dresden
Some Dialects Are More Equal Than Others: Non-Prestigious Arabic Dialectal Bias in LLMs
Sep 21, 2026Mai Mohamed Eida, Ryan Dolan, Paul de Nijs +1Dialect Bias in Language Models Language Model Bias Evaluation
University of Illinois Urbana Champaign
From a River in Gilead to the Inference Distributions of Large Language Models: Covert Dialect Bias and Linguistic Profiling at Scale
Sep 16, 2026Chowdhury Mohammad Abdullah, Rita OrjiSocial Bias in Language Models Dialect Bias in Language Models
Faculty of Computer Science, Dalhousie University Halifax, NS, Canada
The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline
Aug 24, 2026ElleDialect Bias in Language Models Linguistic Bias in Language Models
University of Oxford, Department of Computer Science
DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation
Jul 8, 2026Jordan Painter, Dipankar Srirag, Adarsh Kappiyath +3Language Modeling LLM Alignment
Institute for People-Centred AI, University of Surrey, Surrey, United Kingdom · University of New South Wales, Sydney, Australia
LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation Steering
Jul 7, 2026Huan Wu, Ali Emami, Muhammad Furquan Hassan +5Language Model Steering Social Bias in Language Models
York University · Vector Institute · Connected Minds +6
Can Dialects Be Steered Like Languages? Sparse Neurons and Distributed Directions in Arabic LLMs
Jul 4, 2026Kareem Elozeiri, Mervat Abassy, Omar Kallas +4Arabic NLP LLM Interpretability
1Mohamed bin Zayed University of Artificial Intelligence · 2Qatar Computing Research Institute, Hamad Bin Khalifa University · 3Tohoku University +1
Side-by-side Comparison Amplifies Dialect Bias in Language Models
May 23, 2026Kritee Kondapally, Claire J. Smerdon, Pooja C. Patel +5Social Bias in Language Models Algorithmic Fairness
University of Southern California, USA
Dialect vs Demographics: Quantifying LLM Bias from Implicit Linguistic Signals vs. Explicit User Profiles
Apr 22, 2026Irti Haq, Belén SaldíasLanguage Model Safety Evaluation Social Bias in Language Models
University of Washington, USA
DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects
Apr 7, 2026Jason Lucas, Matt Murtagh, Ali Al-Lawati +3Fake News Detection Dialect Bias in Language Models
The Pennsylvania State University, USA · Trinity College Dublin, Ireland · MIT Lincoln Laboratory, USA
Benchmarking Bengali Dialectal Bias: A Multi-Stage Framework Integrating RAG-Based Translation and Human-Augmented RLAIF
Mar 22, 2026K. M. Jubair Sami, Dipto Sumit, Ariyan Hossain +1Social Bias in Language Models Dialect Bias in Language Models
Department of Computer Science and Engineering BRAC University, Dhaka, Bangladesh
Analysis Of Linguistic Stereotypes in Single and Multi-Agent Generative AI Architectures
Mar 19, 2026Martina Ullasci, Marco Rondina, Riccardo Coppola +6Dialect Bias in Language Models Linguistic Bias in Language Models
Politecnico di Torino Turin, Italy
Evaluating the Usage of African-American Vernacular English in Large Language Models
Feb 25, 2026Deja Dunlap, R. Thomas McCoyDialect Bias in Language Models Linguistic Bias in Language Models
Yale University, New Haven CT 06511, USA
Cross-Lingual Sentiment Misalignment: Auditing Multilingual Language Models for Inversion Risk, Dialectal Representation, and Affective Stability
Feb 19, 2026Nusrat Jahan Lia, Shubhashis Roy DiptaDialect Bias in Language Models Cross-Lingual Consistency in Language Models
Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh · University of Maryland, Baltimore County, Baltimore, Maryland, USA
Stylistic Evolution and LLM Neutrality in Singlish Language
Jan 10, 2026Linus Tze En Foo, Weihan Angela Ng, Wenkai Li +1Dialect Bias in Language Models Language Model Bias Evaluation
Independent Researcher, Singapore, Singapore · ETH Zürich, Zürich, Switzerland · Carnegie Mellon University, Pittsburgh, US

Oct 20, 2026J/K move · Enter open · S save

Answering in the User's Variety: Calibrated Accommodation Pairs for Post-Training

Zhizhuo Kou, Heming Li, Jian Yang, Junyu Luo+3

1The Hong Kong University of Science and Technology · 2The Hong Kong University of Science and Technology (Guangzhou) · Peking University

Dialect Bias in Language Models Language Model Post-Training Language Model Bias Evaluation Direct Preference Optimization

Open paper AskPDF arXiv

People adapt how they talk to the person in front of them. However, chat models tend to respond in Standard English regardless of the dialect used by the user. A uniform answer can hide whether training treats these users alike, and output audits cannot tell as well. We trace how each post-training stage, from supervised fine-tuning (SFT) to preference optimization (DPO) and reinforcement learning, changes a model's tendency to match the user, and read what each update prefers from two content-matched replies, one matching the user and one not. Across 10 open model lineages, post-training standardizes replies to every informal cue, yet after adjusting for how likely each reply already was, the DPO update still favours matching British and Australian spelling over AAE and Singlish on 10 lineages, and models still produce AAE features when asked. We then introduce calibrated accommodation pairs (CAP), preference data that reward matching users' dialect density while preserving Standard English for Standard-English users. On a fully open model, CAP raises convergence on features it was not trained on without a detectable increase in dialect for Standard-English users or a cost in instruction following. In contrast, one-sided pairs reach a similar gain only by overshooting the user and imposing dialect. Our findings show that uniform replies can hide selective updates and audits should read the update as well as the output. Code: https://github.com/kouzhizhuo/cap-accommodation.