The paper "Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems" investigates how commercial text‑to‑image models silently modify user prompts before generating images, a step that is often hidden from users. Using the multilingual benchmark WORLDVIEW, the authors audit the revision layer in DALL‑E‑3, Imagen‑4, and GPT‑Image‑1.5, finding that non‑Western and non‑Anglophone contexts are disproportionately marked, reduced to narrow vocabularies, and stereotyped. The study demonstrates that the revision layer itself is a previously undocumented causal source of cultural stereotyping, underscoring the need to audit deployed systems rather than just the underlying models.
By Aleksandra Urman, Elsa Lichtenegger, Salima Jaoua, Azza Bouleimen, Robin Forsberg, Corinna Hertweck, Stefania Ionescu, Nicol\`o Pagan, Ancsa Hannak, Joachim Baumann
arXiv:2607. 19243v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain disproportionately biased toward high-resource languages.
By Alexander Manev
arXiv:2608.20351v1 Announce Type: cross
Abstract: We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) sy...
By Yanhang Li, Zhichao Fan, Zexin Zhuang
arXiv:2601. 14063v2 Announce Type: replace-cross Abstract: Cross-cultural competence in large language models (LLMs) requires understanding and adapting Culture-Specific Items (CSIs) across varying cultural contexts.
By Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman, Shaoxiong Ji, Hassan Alhuzali, Yuechen Jiang, Jimin Huang, Sophia Ananiadou
Camellia is a new benchmark that tests cultural bias in large language models (LLMs) across nine Asian languages and six Asian cultures. It contains 19,530 manually annotated entities linked to Asian or Western cultures and 2,173 masked social‑media contexts for these entities. Using Camellia, the authors evaluate four multilingual LLMs on cultural context adaptation, sentiment association, and entity extractive QA, finding that models struggle with cultural adaptation, exhibit differing biases across regions and families, and have difficulty understanding context in some Asian languages.
By Tarek Naous, Anagha Savit, Carlos Rafael Catalan, Geyang Guo, Jaehyeok Lee, Kyungdon Lee, Lheane Marie Dizon, Mengyu Ye, Neel Kothari, Sahajpreet Singh, Sarah Masud, Tanish Patwa, Trung Thanh Tran, Zohaib Khan, Alan Ritter, Tanmoy Chakraborty, Yuki Arase, Keisuke Sakaguchi, JinYeong Bak, Wei Xu
arXiv:2606. 12443v1 Announce Type: cross Abstract: Social roles shape expectations, priorities, and judgments, yet it remains unclear how large language models (LLMs) associate occupational identities with broader cultural value patterns.
By Maksim E. Eren, Andrea Brennen, Ryan C. Barron, Eric Michalak
The paper examines whether removing declared language fields from de‑identified résumés eliminates demographic leakage in large language models. By keeping language attributes identical and varying only unstructured prose across five ethnocultural groups and three cue‑salience levels, the authors find that non‑language text still allows target‑group recovery (average 0.757, reaching 1.000 under high salience). They also show that evaluation design—such as allowing or forbidding ties—dramatically affects LLM‑as‑a‑judge outcomes, underscoring the importance of evaluation protocol in bias audits.
By Qiangju Chen, Yang Xiao
arXiv:2609.00565v1 Announce Type: cross
Abstract: Cultural fine-tuning has become the de facto paradigm for building culture-aware large language models (LLMs), yet existing optimization exclusively...
By Jingshen Zhang, Shaoyang Xu, Wenxuan Zhang
The paper investigates gender bias in machine translation evaluation metrics using an occupation-balanced subset of GAMBIT+ across seven English‑source language pairs, including a new German extension. It finds that masculine translations tend to receive higher scores and that biases align with stereotypical gender representations, though the strength varies by evaluator and language. The study highlights that assessing bias requires multiple dimensions beyond a single aggregate measure.
By Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Wafaa Mohammed, Giuseppe Attanasio, Chrysoula Zerva
arXiv:2609.00491v1 Announce Type: new
Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect larg...
By Hangxiao Zhu, Suliu Qin, Zhuoyan Li, Ming Jiang, Yu Zhang, Meng Xia
arXiv:2606. 01260v1 Announce Type: cross Abstract: Despite being home to more than 1300 ethnic groups and 700 indigenous languages, bias in Large Language Models has not been fully studied in Indonesia, thus leaving a critical gap in evaluating representational fairness and localized stereotypes within its uniquely vast, multilingual, and diverse sociocultural landscape.
By Ikhlasul Akmal Hanif, Muhammad Falensi Azmi, Filbert Aurelian Tjiaranata, Eryawan Presma Yulianrifat, Fajri Koto
CuBEs introduces culturally‑situated behavioral evaluations for large language models, adding cultural context to test scenarios and assessments. The authors built a human‑labeled dataset covering 12 cultures, revealing significant cross‑cultural differences that one‑size‑fits‑all judgments miss. Evaluating 13 LLMs shows that culturally situated tests uncover varied behaviors, such as Western political bias versus non‑Western religious or colonial biases, which standard evaluations overlook.
By Hoda Ayad, Tanu Mitra, Abhishek Mukherji