VQV Signal
An observational study examined around 2 million citations from four commercial large language models across 10,000 web pages to identify features influencing citation frequency. The research covers data from ChatGPT, Claude, Google AI, and Gemini over six months.
An observational study examined around 2 million citations from four commercial large language models across 10,000 web pages to identify features influencing citation frequency. The research covers data from ChatGPT, Claude, Google AI, and Gemini over six months.
AI-assisted summary based on the listed source.
Production large language models retrieve and cite web pages alongside generated answers, yet the page-level features that predict citation frequency remain poorly characterised. We present an observational study of approximately 2 million LLM citations from four commercial engines (ChatGPT, Claude, Google AI,...
Understanding what drives citation frequency helps clarify how LLMs source and prioritize information, which is crucial for evaluating their reliability and transparency. This insight can guide improvements in AI-generated content attribution.
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to Consumer AI, connected to arXiv.
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