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#small language model Open access

Which Publications Do AI Assistants Cite When Recommending Businesses? An Exploratory Study

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research) · 1 references
AI in Service Interactions

Abstract

Consumers increasingly ask conversational large language models (LLMs) which product or company to choose. When web-search-enabled assistants answer, they ground their recommendations in sources retrieved at query time, yet little is documented about which publications they draw on. We conducted an exploratory study of the sources cited by two widely used assistants, OpenAI's ChatGPT and Google's Gemini, across 100 open "best/top X for a small business" questions spanning ten commercial categories, issued twice to each assistant (398 usable responses). For every response we recorded the source domains the assistant grounded on, excluding search-action links, and separated editorial publications from platform properties. The two assistants drew on almost entirely different sources: among editorial domains cited in three or more responses, their overlap was only 12%. ChatGPT leaned on technology-review and financial press (TechRadar was its most-cited source, in 16% of its responses; followed by CNBC, Tom's Guide, and Yahoo Finance). Gemini grounded heavily on platform properties (YouTube in 53% of its responses, Reddit in 25%) and on consumer-review and comparison sites (Forbes, Trustpilot, ConsumerAffairs, G2). Press-release wire domains appeared in under 3% of responses, marginally more often via ChatGPT. Important limitation: Gemini's grounding interface returns redirect labels rather than verifiable source URLs, so its reported sources cannot be independently confirmed, and part of the observed divergence reflects how each platform reports sources, not only what it reads. We release the full dataset. Given the exploratory sample, results are directional, not definitive.

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