LLMO Research Papers

“GEO: Generative Engine Optimization”

arXiv:2311.09735, 16th November 2023

Authors

Pranjal Aggarwal♢ Vishvak Murahari ♠ Tanmay Rajpurohit† Ashwin Kalyan‡ Karthik R Narasimhan♠ Ameet Deshpande♠
♠ Princeton University † Georgia Tech ‡ The Allen Institute for AI ♢ IIT Delhi

Key Findings

This study looked at seven potential ‘methods’ for modifying content such that, assuming the content is included in the context passed to an LLM as part of a RAG-based search system, it will be more likely to be cited prominently in the output of that LLM. It found a number of such methods to be effective at doing that, in one case boosting source visibility by as much as 40%.

The most effective methods were:

  • adding citations
  • adding quotations from relevant sources
  • adding statistics

The effectiveness of different methods varied by the domain of the query.

Potential Weaknesses of Study

  • The study focused on the goal of increasing the visibility of citations. It’s unclear if this will be the most relevant goal for the majority of content creators. For example, it may be more important to have their brand mentioned in a favourable way.
  • The study’s formula for calculating the visibility of citations may or may not reflect visibility to users in practice.
  • Modifying content in the ways mentioned may well affect the likelihood of it being included in the context passed to LLMs in the first place. The study doesn’t explore this.

Practical Takeaways

When creating new content it may well be worth putting extra emphasis on including authority-boosting elements such as citations, quotations from relevant sources, and statistics.