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: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
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
As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current literature suffers from widespread methodological fragmentation, which yields contradictory conclusions.
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
The paper presents a multilingual audit of stereotype bias in large language models (LLMs) using 12,000 locally written Reddit posts in English, Spanish, French, and German. It evaluates gender, racial, and religious bias across six models (three open-weight and three proprietary APIs) with a two-blank design to reduce grammatical cues, finding that religion shows the highest imbalance, Spanish leads in gender bias, and German in race and religion. The study highlights that aggregate scores can mask category- and context-specific biases, underscoring the need for detailed inspection before deploying LLMs in consequential settings.
By Zihao Sun