Chinese-Language Generative Search Engines: A Large-Scale Empirical Study
A large-scale empirical study of Chinese-language generative search across four platforms and eight interfaces analyzed over 160,000 citation-level records to examine citation behavior, source attribution, and entity exposure. The study found that brands are selectively surfaced in answers (8.3% selection rate), with content fit and cross-source occurrence being key predictors, while the 5118-Baidu Composite Quality Score was not a leading predictor. Cited pages had short half-lives (39-68 days), and source sets differed systematically between App and Web interfaces. Additionally, a notable proportion of brand and contact information exposures could not be matched to the citation pool or crawled text.
Why it matters: This study provides the first large-scale empirical characterization of how Chinese-language generative search systems select, attribute, and surface information, revealing important interface-specific biases and citation behaviors.
Full story at: arXiv Information Retrieval ↗