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New paper analyzes how prompt corpora shape AI answer market visibility scores

By PulseAugur Editorial · [1 sources] ·

A new paper on arXiv explores the concept of "generative engine optimization" (GEO) visibility scores, which aggregate source appearances, citations, or brand mentions in AI-generated answers. The research highlights that the prompt corpus and weighting systems used to define these scores create an "answer market" that may not reflect actual user demand. The paper proposes a framework for analyzing how prompt wording, scoring identification, and language model instructions can influence these scores, distinguishing between values compatible with data and variations across weighting conventions. AI

IMPACT This research provides a framework for understanding and evaluating how AI systems are perceived and ranked, impacting the development of more transparent and user-aligned generative models.

RANK_REASON The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New paper analyzes how prompt corpora shape AI answer market visibility scores

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The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Olivier Martinez ·

    Measuring GEO Visibility: Prompt Corpora Define the Answer Market

    GEO (generative engine optimization) visibility scores aggregate source appearances, citations, or brand mentions in generated answers. The prompt corpus selects the situations evaluated, while weights determine their relative importance. Together they define an "answer market" t…

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