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Unintended Effects of Geographic Conditioning in Large Language Models
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unintended-effects-of-geographic-conditioning-in-large-language-models-ea82408c·1 events·first seen 6h agoAliases: Unintended Effects of Geographic Conditioning in Large Language Models
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Location metadata causes systematic geographic bias leakage in LLMs, even with 'Unknown' placeholders
Researchers evaluate 'location leakage' — the phenomenon where LLMs generate geographically biased outputs when exposed to location metadata in user profiles, even when prompts are geographically neutral. Across creative writing and Q&A tasks, leakage spikes up to 793x above baseline for models including Llama 3.1-8B, Qwen3-8B, and Claude Sonnet 4.6. A novel structural finding shows that replacing location with 'Unknown' still elevates leakage by up to 72x, indicating the user profile frame itself acts as a conditioning signal independent of geographic content. This has direct implications for AI systems that use user metadata for localization.