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    <abstract>AI-generated overviews are becoming an increasingly prominent layer of search interfaces, yet their behavior in Chinese-language search remains underexplored. We conduct a cross-language audit of AI overview behavior on Baidu and Google using English queries sampled from MS MARCO and their translated Chinese counterparts. Our analysis examines when overviews are triggered across platform-language settings, which host domains receive visible exposure in Chinese overviews, how concentrated that exposure is, and how source overlap varies across settings. We also compare the embedding-based semantic similarity of generated answers for matched query intents. The results reveal substantial differences across platform-language settings in overview availability and visible source exposure. At the aggregate level, the settings exhibit low overlap in visible host-domain inventories, while matched-query answers remain broadly semantically similar. These findings indicate that answer-level semantic similarity and aggregate source exposure capture distinct dimensions of AI-mediated search. Evaluations of AI search should therefore consider not only the content of generated answers but also how source visibility is distributed across platforms, languages, and information environments.</abstract>
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%0 Conference Proceedings
%T Auditing Source Exposure in Baidu and Google AI Search
%A Li, Yibo
%A Guan, Enci
%A Cai, Yuedan
%A Liu, Geng
%A Pierri, Francesco
%Y Ortiz Suarez, Pedro
%Y Burchell, Laurie
%Y Ljubešić, Nikola
%Y Scherrer, Yves
%Y Laippala, Veronika
%Y Dinić, Vuk
%Y Vaughan, Thom
%S Proceedings of the 13th Web-as-Corpus Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-481-1
%F li-etal-2026-auditing
%X AI-generated overviews are becoming an increasingly prominent layer of search interfaces, yet their behavior in Chinese-language search remains underexplored. We conduct a cross-language audit of AI overview behavior on Baidu and Google using English queries sampled from MS MARCO and their translated Chinese counterparts. Our analysis examines when overviews are triggered across platform-language settings, which host domains receive visible exposure in Chinese overviews, how concentrated that exposure is, and how source overlap varies across settings. We also compare the embedding-based semantic similarity of generated answers for matched query intents. The results reveal substantial differences across platform-language settings in overview availability and visible source exposure. At the aggregate level, the settings exhibit low overlap in visible host-domain inventories, while matched-query answers remain broadly semantically similar. These findings indicate that answer-level semantic similarity and aggregate source exposure capture distinct dimensions of AI-mediated search. Evaluations of AI search should therefore consider not only the content of generated answers but also how source visibility is distributed across platforms, languages, and information environments.
%U https://aclanthology.org/2026.wac-1.8/
%P 88-99

Markdown (Informal)

[Auditing Source Exposure in Baidu and Google AI Search](https://aclanthology.org/2026.wac-1.8/) (Li et al., WAC 2026)

• Auditing Source Exposure in Baidu and Google AI Search (Li et al., WAC 2026)

ACL

• Yibo Li, Enci Guan, Yuedan Cai, Geng Liu, and Francesco Pierri. 2026. Auditing Source Exposure in Baidu and Google AI Search. In Proceedings of the 13th Web-as-Corpus Workshop, pages 88–99, Budapest, Hungary. Association for Computational Linguistics.