Real ChatGPT answer · index seedmL0FtEZWmaUcompleteMarketing and advertising · AI search and generative engine optimizationinformational
Can you outline the theoretical framework behind how LLMs weight primary source data versus aggregated content when deciding which URL to cite in an answer?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can you outline the theoretical framework behind how LLMs weight primary source data versus aggregated content when deciding which URL to cite in an answer?
1 real search · 1 real-search pass
LLM citation source selection primary sources secondary sources retrieval augmented generation citation quality source authority researchGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
LLM citation source selection primary sources secondary sources retrieval augmented generation citation quality source authority research Google ↗
- Synthesizing scientific literature with retrieval-augmented language models · nature.com
- Synthesizing scientific literature with retrieval-augmented language models · doi.org
- Retrieval-Augmented Generation with Estimation of Source Reliability · aclanthology.org
- How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation · arxiv.org
- How Do LLMs Cite? · mariaheuss.com
- [2605.06635] Cited but Not Verified: Parsing and Evaluating Source Attribution in LLM Deep Research Agents · doi.org
- Cited but Not Verified: Parsing and Evaluating Source Attribution in LLM Deep Research Agents · arxiv.org