The paper introduces WinoQueer‑NL, a Dutch adaptation of the English WinoQueer benchmark, designed to assess anti‑queer bias in Dutch language models. After validating the dataset with 43 queer Dutch participants, the authors released 42,906 sentences and evaluated several Dutch‑specific and multilingual models, finding that while overall bias scores were neutral, certain models disproportionately favored stereotypical statements for transgender and non‑binary identities. The study underscores the need for culturally grounded datasets to identify and mitigate biases that affect marginalized groups in Dutch NLP systems.
The paper introduces a German-English benchmark dataset to evaluate anti‑LGBTQ biases in language models, combining community‑sourced stereotypes from German‑speaking queer individuals with a German translation of WinoQueer. Eight language models of varying sizes and architectures were assessed, revealing that they reproduce anti‑queer stereotypes with differences across identities and models. Fine‑tuning on community and progressive media content reduced bias on average, though the effect was not consistent across all models and identities.
By Melina Morch, Daniel Braun
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
arXiv:2604.20677v3 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive settings, raising concerns about fairness and bias, particularly when...
By Chaima Boufaied, Ronnie De Souza Santos, Ann Barcomb
arXiv:2502.10577v2 Announce Type: replace-cross
Abstract: Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constra...
By Enzo Doyen, Amalia Todirascu
MBBQ (Multilingual Bias Benchmark for Question-answering) is a curated extension of the English BBQ dataset that includes Dutch, Spanish, and Turkish, designed to measure stereotypes across these languages while controlling for cultural differences and task accuracy. The dataset also provides a parallel control set to assess question-answering performance independently of bias. Experiments with several open-source and proprietary LLMs show that non‑English languages can exhibit greater bias than English, and cross‑lingual bias differences persist except in the most accurate models.
By Vera Neplenbroek, Arianna Bisazza, Raquel Fern\'andez
arXiv:2609.38036v2 Announce Type: replace-cross
Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support to...
By Edoardo Bolzoni, Valerio Capraro
The paper introduces GPTBIAS, a framework that uses powerful large language models like GPT‑4 to evaluate bias in other LLMs. It employs specially crafted prompts called Bias Attack Instructions to probe for bias and outputs a bias score along with detailed information such as bias types, affected demographics, keywords, reasons, and improvement suggestions. Extensive experiments demonstrate the framework’s effectiveness and usability.
By Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin, Mykola Pechenizkiy
arXiv:2610.22545v1 Announce Type: new
Abstract: Stereotypes are subjective constructs that are mediated by the cultural context in which they are viewed. Successfully modeling them thus also requires...
By Maximilian Splieth\"over, Jannes L\"otfering, Dominik Bachmann, Alexander Espig, Willem Zuidema, Katrin Schulz, Henning Wachsmuth
arXiv:2609.38036v1 Announce Type: cross
Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with...
By Edoardo Bolzoni, Valerio Capraro
arXiv:2604. 01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly.
By Bhaskara Hanuma Vedula, Darshan Anghan, Ishita Goyal, Ponnurangam Kumaraguru, Abhijnan Chakraborty
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
By Petter T\"ornberg