mlsanityData to 14 Oct 2026

What Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pages

arXiv 2609.35077 ↗First posted 28 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

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, Gemini) over six months, joined to 10,000 crawled pages from nineteen B2B SaaS workspaces. Sixty-plus features are tested using a nine-method consensus framework combining mixed-effects regression with domain fixed effects, FDR correction, stability-selection Lasso, double machine learning, generalised additive models, and temporal hold-out replication. Four findings survive all checks. First, prompt-content alignment (Jaccard overlap between page tokens and the full workspace prompt corpus, including non-citing prompts) is the dominant page-level predictor (beta = +0.37, 95% CI [+0.33, +0.41], q ~ 10^-73). Second, the standard AEO checklist (FAQ blocks, structured data, Core Web Vitals) shows positive effects in pooled data that reverse or collapse to zero once domain fixed effects are applied: Simpson's paradox with practical consequences for the AEO literature. Third, domain-level AI authority exceeds the strongest non-alignment page-level feature by a factor of six in mean absolute SHAP value. We release the analytic pipeline as a methodological contribution.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 28 Sep 2026.

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References · 15

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  5. 2023Evaluating Verifiability in Generative Search Enginescited by 303
  6. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  7. 2023How is ChatGPT's behavior changing over time?cited by 270
  8. 2017How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utilitycited by 120
  9. 2022Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Modelscited by 112
  10. 2023GEO: Generative Engine Optimizationcited by 56
  11. 2024Manipulating Large Language Models to Increase Product Visibilitycited by 28
  12. 2025News Source Citing Patterns in AI Search Systemscited by 10
3 more
  1. 2025Source Coverage and Citation Bias in LLM-based vs. Traditional Search Enginescited by 6
  2. 2026Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behaviorcited by 5
  3. 2026From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platformscited by 1

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