对比大模型与传统搜索引擎的来源覆盖与引用偏见
Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines
- 分析6种大模型搜索和2种传统搜索的5.6万次查询结果
- 大模型搜索覆盖37%独有领域资源，但可信度未更高
- 揭示大模型选源的关键影响因素，助用户与开发者优化
基于大语言模型的搜索引擎（LLM-SEs）开启了信息获取的新范式。与传统搜索引擎（如Google）不同，它们通常对结果进行摘要，且引用透明度较低。这一转变的影响尚未充分研究，却关乎信任与透明性。本文开展大规模实证研究，分析了6种LLM-SEs和2种传统搜索引擎在55,936个查询下的结果。结果显示，LLM-SEs在引用领域资源上具有更广多样性，37%的域名仅出现在LLM-SEs中。然而，在可信度、政治中立性和安全性等指标上，LLM-SEs并未优于传统搜索引擎。为进一步理解其选源机制，我们进行了基于特征的分析，识别出影响来源选择的关键因素。研究为终端用户、网站所有者及开发者提供了可操作的洞见。
原文摘要 · Abstract (English)
LLM-based Search Engines (LLM-SEs) introduces a new paradigm for information seeking. Unlike Traditional Search Engines (TSEs) (e.g., Google), these systems summarize results, often providing limited citation transparency. The implications of this shift remain largely unexplored, yet raises key questions regarding trust and transparency. In this paper, we present a large-scale empirical study of LLM-SEs, analyzing 55,936 queries and the corresponding search results across six LLM-SEs and two TSEs. We confirm that LLM-SEs cites domain resources with greater diversity than TSEs. Indeed, 37% of domains are unique to LLM-SEs. However, certain risks still persist: LLM-SEs do not outperform TSEs in credibility, political neutrality and safety metrics. Finally, to understand the selection criteria of LLM-SEs, we perform a feature-based analysis to identify key factors influencing source choice. Our findings provide actionable insights for end users, website owners, and developers.
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