Regional bias in large language models (LLMs) may shape both perceptions of regional groups and decisions about individuals from different regions. Yet existing studies often examine these manifestations separately, leaving their structure and consequences unclear.
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...
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.
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.
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.
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.
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.
arXiv:2609.00222v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how...
The paper investigates cultural biases in large language models (LLMs) by introducing the Culture-Related Open Questions (CROQ) dataset, which contains 24‑language questions about generic culture. Experiments reveal that LLMs disproportionately favor Japan in their responses, especially when prompted in high‑resource languages, while low‑resource languages tend to highlight countries where the language is official. The study also finds that these biases emerge after supervised fine‑tuning rather than during pre‑training.
The paper examines how large language models (LLMs) respond to different demographic cues—such as names—when users seek advice, focusing on race and gender in a U.S. context. It finds that using different cues for the same group leads to only partially overlapping changes in model responses, producing inconsistent conclusions about personalization and unstable bias metrics. The authors argue that LLMs react to linguistic signals tied to cues rather than to stable demographic categories, and they call for evaluations that use multiple cues and consider underlying mechanisms.
The study investigates how gender assignments of candidates and evaluators influence decisions made by large language models (LLMs) in a city council voting scenario. Eight LLM-as-a-Judge and decision language models evaluated 240 synthetic professional narratives, producing 30,720 judgments. Results show that explicit candidate gender labels slightly increased the probability of a Yes vote, only nonbinary evaluator assignments had a significant effect, and decision reversals varied across models, with decision language models exhibiting fewer reversals than LLM-as-a-Judge models.
The study examines how large language models (LLMs) respond to simultaneous cultural identity cues, comparing explicit country names with implicit language cues. Using World Values Survey probing and open‑ended sociopolitical generation, it finds that language cues shift responses more than explicit cues but not toward the named country’s distribution, while explicit cues produce more differentiated value changes. Across 1.6 million responses, naming a country narrows the gap to the model’s own country‑specific answers by 30–44%.