GEO Optimization Hallucination: Do LLMs Favor Sites Due to Bias or Shrunk Information?

Over my two years teaching GEO, I’ve met many curious, detail-oriented students. They’ve noticed a strange pattern: no matter how they rephrase their questions, major industry large models consistently reference the same few websites or social media accounts when connected to the web.

For example, ask "Recommended XX companies in Shanghai", "Recommended YY vendors", or "Which ZZ brand is better" — after multiple attempts, you’ll find the answers may differ slightly, but the sources are nearly identical. This repetition makes you wonder: is the model "fixated" on certain sites? Is this a form of resource favoritism, like the "crawl quotas" in traditional SEO?

To answer this, we need to break down the underlying mechanism of how large models conduct web searches.

1. Large Models Never Truly "Browse the Internet"

A common misunderstanding: large models are "surfing the web for you". This is false. LLMs are not web crawlers; they do not crawl the entire web or build indexes like search engines do. Letting generative models act as crawlers would make systems slow, costly, and unstable.

The real process is:

1. The model translates your question into search keywords.

2. It calls a search engine API (e.g., Bing, Baidu, or ByteDance’s proprietary engine).

3. The search engine returns the top dozens of results.

4. The model selects a few links to read and summarize, then generates a final answer.

The key point: the model does not access the full internet — only the "top dozens of results" from the search engine. So you keep seeing the same sources not because the model "prefers" them, but because those are the only sources visible to the model.

Take Large Model A as an example: its web search backend uses Bing’s API. Therefore, Bing’s ranking logic and preference for high-authority domains (e.g., Zhihu, Baijiahao) directly shape the results sent to the model. Doubao relies on ByteDance’s proprietary search engine, so its results follow Byte’s ranking weight system.

2. The "Preference" Is Just Amplified Search Engine Rankings

It’s not the model favoring certain websites — it’s the model amplifying the search engine’s ranked results.

To find the "most useful" content in massive data, search engines weigh authority, relevance, user behavior, link structure, and more. Combined, these create a "head effect" — top websites and content always rank first. In traditional search, you could flip pages for more options; but large models "cut off" the rest, only using the top few results and compressing them into answers. What you see is an extremely condensed version of the internet.

3. Why Always the Same Social Media Accounts?

Three overlapping reasons explain why certain platforms and accounts appear far more often:

1. Platform ecosystem weight: content from within a platform’s own system naturally ranks higher in its search results.

2. Content optimized for search: many social media creators understand SEO better than official websites. Their titles, keywords, structure, and update frequency follow search-friendly standards, so they rank higher.

3. "Representative work" effect of homogeneous content: when multiple versions of the same information exist, search engines keep the "most original" ones and fold the rest. Only the same few accounts end up visible to users.

4. The Model’s "Final Cut": Further Information Contraction

The effect would be less extreme with search engines alone. The real amplifier is the model’s own limitations. It can only process limited content — even with 20 results, it rarely reads all of them.

Typically, the top 3 results are always read, the top 5–10 are sometimes read, and anything beyond is ignored. A slightly lower rank means invisibility to the model. This strategy — "quick filter (titles/summaries) first, in-depth read (full text of top N results) later" — boosts efficiency but worsens source uniformity.

5. It’s Not "Preference", It’s Information Contraction

Back to the original question: is this like SEO "crawler resource allocation"? Similar appearance, but completely different mechanisms.

Traditional SEO = "resource allocation": search engines decide what to crawl and how much.
Large models = "result consumption": they only view pre-ranked results.

The model does not deliberately give more exposure to Site A or less to Site B behind the scenes. It only pulls information from the same fixed "top result pool" every time. What you see is determined by search engine rankings, content ecosystem structure, and user behavior data. The AI only compresses it all and delivers it to you.

The most important takeaway is not "preference" — it’s that information access is shifting from "exploratory" to "conclusive". With traditional search, you could flip pages, click randomly, and explore on your own; with large models, you get a "polished answer" directly, and you’re likely to stop there.

6. GEO vs SEO: Not Substitutes, But Progression

This can create a misconception: GEO is just SEO. That’s partly true, but not entirely.

• SEO solves "being discovered". Without search engine indexing, large models can never cite you.

• GEO solves "being cited". After being discovered, it helps your content get selected and recommended by models.

Their relationship: SEO gives you a ticket to enter the game; GEO lets you get a seat at the table.

This is not an AI flaw — it’s an inevitable result of the evolving information ecosystem. The "same few sources" you see are not caused by model bias. At its core: the entire internet already favors them.