Large-scale analysis of AI engine responses shows brand visibility in three tiers (73%, 44%, 11%) with corporate sites and best-of listicles as top cited sources.
Gummadi, and Muhammad Bilal Zafar
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response. This paradigm differs fundamentally from traditional web search, where results are returned as a ranked list of independent web pages. In this paper, we ask: Along what dimensions does generative search differ from traditional search? We conduct a systematic comparison between Google organic search and five generative search systems from three providers: Google, OpenAI, and Perplexity. Our analysis reveals substantial variation among engines in their reliance on internal v.s. external knowledge, source diversity, and stability. While generative systems often achieve topical coverage comparable to traditional search, they do so using markedly different retrieval footprints and synthesis strategies. We further show that the outputs of generative search can vary across time and executions, raising new challenges for robustness. Our findings demonstrate that generative search introduces new dimensions that are not captured by existing evaluation paradigms, motivating the development of evaluations that explicitly account for retrieval behavior, synthesis, and stability in generative search systems.
citation-role summary
method 1
citation-polarity summary
years
2026 5roles
method 1polarities
use method 1representative citing papers
AI Overviews and Gemini retrieve substantially different sources than traditional Google search (Jaccard similarity <0.2), favor Google-owned content, appear for 51.5% of queries especially controversial ones, and are less consistent across repeated or slightly edited queries.
A measurement study of 602 prompts across ChatGPT, Google AI Overview, and Perplexity finds that citation selection breadth and absorption depth diverge, with high-influence pages being longer, structured, and evidence-rich.
LLMs cite third-party domains for 85.7% of brand attributions, with Wikipedia dominant in most languages, a long-tailed domain distribution, and market-specific shifts such as YouTube and HR sites in Poland.
citing papers explorer
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Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines
Large-scale analysis of AI engine responses shows brand visibility in three tiers (73%, 44%, 11%) with corporate sites and best-of listicles as top cited sources.
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How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews
AI Overviews and Gemini retrieve substantially different sources than traditional Google search (Jaccard similarity <0.2), favor Google-owned content, appear for 51.5% of queries especially controversial ones, and are less consistent across repeated or slightly edited queries.
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From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms
A measurement study of 602 prompts across ChatGPT, Google AI Overview, and Perplexity finds that citation selection breadth and absorption depth diverge, with high-influence pages being longer, structured, and evidence-rich.
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How Large Language Models Source Brand Reputation Across Languages and Markets
LLMs cite third-party domains for 85.7% of brand attributions, with Wikipedia dominant in most languages, a long-tailed domain distribution, and market-specific shifts such as YouTube and HR sites in Poland.
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