# When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine

arXiv: https://arxiv.org/abs/2509.14436 · HTML: https://mlsanity.com/paper/2509.14436/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [The Vendi Score: A Diversity Evaluation Metric for Machine Learning](https://mlsanity.com/paper/2210.02410/) (2022)
- [Demystifying Prompts in Language Models via Perplexity Estimation](https://mlsanity.com/paper/2212.04037/) (2022)
- [BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text](https://mlsanity.com/paper/2403.18421/) (2024)
- [Emergent Abilities in Large Language Models: A Survey](https://mlsanity.com/paper/2503.05788/) (2025)
- [The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot](https://mlsanity.com/paper/2409.08379/) (2024)
- [ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience](https://mlsanity.com/paper/2307.01135/) (2023)
- [AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform](https://mlsanity.com/paper/2312.04180/) (2023)
- [Learning to Adopt Generative AI](https://mlsanity.com/paper/2410.19806/) (2024)

## Built on by (most cited first, 2 of 2)

- [RADAR: Readiness for AI Discovery and Agentic Reach](https://mlsanity.com/paper/2609.28480/) (2026)
- [Understanding Generative AI-mediated User Engagement with Academic Library Resources](https://mlsanity.com/paper/2607.20328/) (2026)

## Abstract (arXiv)

Generative search engines (GEs) leverage large language models (LLMs) to deliver AI-generated summaries with website citations, establishing novel traffic acquisition channels while fundamentally altering the search engine optimization landscape. To investigate the distinctive characteristics of GEs, we collect data through interactions with Google's generative and conventional search platforms, compiling a dataset of approximately ten thousand websites across both channels. Our empirical analysis reveals that GEs exhibit preferences for citing content characterized by significantly higher predictability for underlying LLMs and greater semantic similarity among selected sources. Through controlled experiments utilizing retrieval augmented generation (RAG) APIs, we demonstrate that these citation preferences emerge from intrinsic LLM tendencies to favor content aligned with their generative expression patterns. Motivated by applications of LLMs to optimize website content, we conduct additional experimentation to explore how LLM-based content polishing by website proprietors alters AI summaries, finding that such polishing paradoxically enhances information diversity within AI summaries. Finally, to assess the user-end impact of LLM-induced information increases, we design a generative search engine and recruit Prolific participants to conduct a randomized controlled experiment involving an information-seeking and writing task. We find that higher-educated users exhibit minimal changes in their final outputs' information diversity but demonstrate significantly reduced task completion time when original sites undergo polishing. Conversely, lower-educated users primarily benefit through enhanced information density in their task outputs while maintaining similar completion times across experimental groups.