Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks, leading to their widespread deployment. However, recent studies have highlighted concerning biases in these models, particularly in their handling of dialectal variations like African American English (AAE). In this work, we systematically investigate dialectal disparities in LLM reasoning tasks. We develop an experimental framework comparing LLM performance given Standard American English (SAE) and AAE prompts, combining LLM-based dialect conversion with established linguistic analyses. We find that LLMs consistently produce less accurate responses and simpler reasoning chains and explanations for AAE inputs compared to equivalent SAE questions, with disparities most pronounced in social science and humanities domains. These findings highlight systematic differences in how LLMs process and reason about different language varieties, raising important questions about the development and deployment of these systems in our multilingual and multidialectal world. Our code repository is publicly available at https://github.com/Runtaozhou/dialect_bias_eval.
Data Caricatures: On the Representation of African American Language in Pretraining Corpora
Columbia University, Department of Computer Science（哥伦比亚大学计算机科学系）；University of Michigan, Department of Linguistics（密歇根大学语言学系）；University of Pennsylvania, School of Social Policy and Practice, Annenberg School for Communications（宾夕法尼亚大学社会政策与实践学院、安纳伯格通讯学院）；University of Tennessee, Knoxville, Department of English（田纳西大学基洛纳分校英语系）
Rejected Dialects: Biases Against African American Language in Reward Models
Carnegie Mellon University（卡内基梅隆大学）；Columbia University（哥伦比亚大学）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；Instituto de Telecomunicações（电信研究所）
LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation Steering
York University（约克大学）；Vector Institute（向量研究所）；Connected Minds（连接思维公司）；Emory University（埃默里大学）；Wilfrid Laurier University（威尔弗里德·劳里埃大学）；University of Toronto（多伦多大学）；University of Guelph（圭尔夫大学）；Monark Health（莫纳克健康公司）；CIFAR Solution Network（加拿大高级研究院解决方案网络）
Advancing Equitable AI: Evaluating Cultural Expressiveness in LLMs for Latin American Contexts
Department of Applied Computing, Michigan Technological University, Houghton, MI, USA（应用计算系，密歇根技术大学，霍顿，密歇根州，美国）；Department of Psychology and Human Factors, Michigan Technological University, Houghton, MI, USA（心理学与人因学系，密歇根技术大学，霍顿，密歇根州，美国）
Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English
Department of English Language and Linguistics, Institute of English and American Studies, Faculty of Arts and Humanities, Heinrich Heine University Düsseldorf, Germany（海因里希·海涅大学杜塞尔多夫分校，人文与艺术学院，英美研究学院，英语语言与语言学系）；Department of Linguistics, University of Florida, United States of America（佛罗里达大学语言学系）
Can Small-Scale Data Poisoning Exacerbate Dialect-Linked Biases in Large Language Models?
Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture（电气与计算机工程系，马鲁恩·塞马安工程与建筑学院）；American University of Beirut（贝鲁特美国大学）
Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
DePaul University（德保罗大学）；University of Washington（华盛顿大学）；Carnegie Mellon University（卡内基梅隆大学）；Cornell Tech University（康奈尔科技大学）；University of Maryland（马里兰大学）
Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages
Johns Hopkins University（约翰斯·霍普金斯大学）；National Institute of Information and Communications Technology（信息通信研究机构）；Brigham Young University（杨百翰大学）；Inria Paris（法国国家信息与自动化研究所巴黎分部）；Nile University of Nigeria（尼日利亚尼罗河大学）；Université Paris Nanterre（巴黎第十大学）；University of Minnesota - Twin Cities（明尼苏达大学双城分校）；University of Toronto（多伦多大学）
ChatGPT Exhibits Gender and Racial Biases in Acute Coronary Syndrome Management
Stanford Cardiovascular Institute（斯坦福心血管研究所）；Stanford School of Medicine（斯坦福医学院）；Greenstone Biosciences（格林斯通生物科学公司）；Stanford School of Engineering（斯坦福工程学院）；NewYork–Presbyterian Hospital/Weill Cornell Medical Center（纽约长老会医院/威尔康奈尔医学中心）
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding
Salesforce AI Research（赛富时人工智能研究院）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
