Understand this story
A paper from the University of Toronto and Google argues that benchmarks for generative engine optimization (GEO) need a unified set of permitted webpage editing rules, otherwise scores cannot be compared. The paper labels each type of GEO edit by where the change is made, how it is made, and the cue it targets (such as authority), and recommends that tests report visibility, answer quality, and rule compliance together. The paper conducted no experiments; its evidence is a review of 11 published GEO methods, showing that the types of edits covered vary from method to method.
AI-generated from public reports
Latest development · Report
University of Toronto and Google paper: GEO evaluation needs unified editing rulesDevelopments
1 confirmed developments
Reports and discussion timeline
Browse reports and discussion by time. Their evidence levels remain distinct.
Oct 25
Rohan Paul
University of Toronto and Google paper: GEO evaluation needs unified editing rules
A paper from the University of Toronto and Google argues that GEO (generative engine optimization) benchmarks need unified rules on permitted webpage edits, otherwise scores cannot be compared. The paper labels each type of GEO edit by where the change is made, how it is made, and the cue it targets (such as authority), and recommends that tests report visibility, answer quality, and rule compliance together. The paper conducted no experiments; its evidence is a review of 11 published GEO methods, showing that the types of edits covered by each method differ.