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This first study of Web search across four major conversational platforms is presented, combining real-world user interactions with controlled experiments using the same platform's models by their APIs (invitro), finding that Web-search decisions vary substantially across platforms and models.
LLMs exhibit surprisingly strong and predictable biases towards specific information sources, even overriding content relevance and explicit instructions.
