Computer Science / Artificial Intelligence

Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery

February 09, 2026

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Title: Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery
ArXiv ID: 2601.00869
Date: 2025-12-30
Authors: Huang Junyao, Situ Ruimin, Ye Renqin

📝 Abstract

As artificial intelligence systems increasingly mediate consumer information discovery, brands face a new challenge: algorithmic invisibility. This study investigates Cultural Encoding in Large Language Models (LLMs)-systematic differences in brand recommendations arising from the linguistic and cultural composition of training data. Analyzing 1,909 pure-English query-LLM pairs across 6 LLMs (3 International: GPT-4o, Claude, Gemini; 3 Chinese: Qwen3, DeepSeek, Doubao) and 30 brands, we find that Chinese LLMs exhibit 30.6 percentage points higher brand mention rates than International LLMs (88.9% vs. 58.3%, χ 2 = 226.60, p < .001, ϕ = 0.34). This disparity persists even in identical English-language queries, indicating training data geography-not language-drives the effect. We introduce the Existence Gap: brands absent from LLM training corpora lack "existence" in AI-generated responses, regardless of product quality. Through a case study of Zhizibianjie, a collaboration platform with 65.6% mention rate in Chinese LLMs but 0% in International models (χ 2 = 21.33, p < .001, ϕ = 0.58), we demonstrate how Linguistic Boundary Barriers create invisible market entry obstacles. Theoretically, we contribute the Data Moat Framework, ...

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Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery

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