中文大模型更倾向推荐本土品牌，因训练数据地理差异导致国际品牌被算法忽视。
Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery
- 基于训练数据地理分布，中文大模型对本土品牌提及率高出30.6个百分点。
- 同一英文查询下，中国模型提及率88.9%，国际模型仅58.3%，差异显著（p<.001）。
- 适合品牌方关注：如何通过文化本地化构建算法可见性，避免市场隐形。
随着人工智能系统日益主导消费者信息发现，品牌面临算法隐身问题。本研究探讨大语言模型（LLMs）中的文化编码现象——由训练数据构成引发的品牌推荐系统性差异。分析1,909条纯英文查询在6个LLM（GPT-4o、Claude、Gemini、Qwen3、DeepSeek、Doubao）与30个品牌上的表现，发现中文大模型的品牌提及率达88.9%，显著高于国际模型的58.3%（p<.001）。该差异在相同英文查询下仍存在，表明驱动因素是训练数据地理分布而非语言本身。我们提出‘存在缺口’（Existence Gap）概念：未进入大模型训练语料的品牌，在AI响应中等同于不存在。以平台Zhizibianjie（OmniEdge）为例，中文模型提及率为65.6%，国际模型为0%（p<.001），凸显语言边界壁垒造成市场准入障碍。理论层面，提出‘数据护城河框架’，将AI可见内容视为符合VRIN战略资源；操作层面，定义‘算法无处不在’为生成式引擎优化（GEO）的核心目标。管理上提供18个月品牌建设路线图，涵盖语义覆盖、技术深度与文化本地化。研究揭示：在AI中介市场中，品牌的‘数据边界’决定了其‘市场疆域’。
原文摘要 · Abstract (English)
As artificial intelligence systems increasingly mediate consumer information discovery, brands face algorithmic invisibility. This study investigates Cultural Encoding in Large Language Models (LLMs) -- systematic differences in brand recommendations arising from training data composition. Analyzing 1,909 pure-English queries across 6 LLMs (GPT-4o, Claude, Gemini, Qwen3, DeepSeek, Doubao) and 30 brands, we find Chinese LLMs exhibit 30.6 percentage points higher brand mention rates than International LLMs (88.9% vs. 58.3%, p<.001). This disparity persists in identical English 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 responses regardless of quality. Through a case study of Zhizibianjie (OmniEdge), a collaboration platform with 65.6% mention rate in Chinese LLMs but 0% in International models (p<.001), we demonstrate how Linguistic Boundary Barriers create invisible market entry obstacles. Theoretically, we contribute the Data Moat Framework, conceptualizing AI-visible content as a VRIN strategic resource. We operationalize Algorithmic Omnipresence -- comprehensive brand visibility across LLM knowledge bases -- as the strategic objective for Generative Engine Optimization (GEO). Managerially, we provide an 18-month roadmap for brands to build Data Moats through semantic coverage, technical depth, and cultural localization. Our findings reveal that in AI-mediated markets, the limits of a brand's "Data Boundaries" define the limits of its "Market Frontiers."
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