The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models
arXiv:2606.11082v1 Announce Type: new Abstract: This study investigates cross-lingual distributional skew (the Shibboleth Effect) in frontier large language models (LLMs) subjected to sustained adversarial conditions. We develop a multi-agent geopolitical wargame, the Cerulean Sea Crisis, a synthetic maritime territorial dispute designed to mirror the structural dynamics of Eastern Mediterranean conflicts. Six frontier models (GPT-4o, Llama-4, Mistral-Large, Gemini-3.1-Pro, Qwen3.6-Plus, and DeepSeek-R1) participate in a between-groups experiment (N = 10 games per arm, K = 5 rounds per game) i
The increasing deployment of frontier LLMs across sensitive domains necessitates immediate understanding of their potential biases, especially under adversarial conditions.
Understanding the cross-lingual distributional skew (Shibboleth Effect) in LLMs reveals inherent biases that could exacerbate geopolitical tensions or create strategic vulnerabilities.
The focus extends beyond general LLM safety to specific, language-dependent biases that can be exploited or manifest in high-stakes geopolitical simulations.
- · AI Safety Researchers
- · Governments investing in AI auditing
- · Developers of robust, multilingual LLMs
- · Unaware users of biased LLMs
- · LLM developers ignoring cross-lingual fairness
- · Organizations relying on single-language AI systems
This research directly highlights biases in leading LLMs when exposed to specific geopolitical narratives across languages.
Increased awareness could lead to demands for more rigorous, geopolitically situated testing and mitigation strategies for LLM biases.
Future international conflicts and diplomatic efforts might inadvertently be shaped or skewed by the unaddressed linguistic biases of AI tools used by involved parties.
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Read at arXiv cs.CL