Awesome-GEO is a comprehensive collection dedicated to research on Generative Engine Optimization (GEO). Designed for content creators and researchers, this resource unifies essential papers and insights to enhance visibility in generative engine responses. Contribute to the list and help enrich this collaborative effort in a growing field.
Welcome to Awesome-GEO, a dedicated collection for research on Generative Engine Optimization (GEO). GEO is an emerging paradigm designed to assist content creators in enhancing their visibility across generative engine responses by employing flexible optimization frameworks to refine and define effective visibility metrics.
As a specialized field gaining traction within the community, Awesome-GEO aims to compile key resources, papers, and findings contributing to this innovative area. This curated list is continually evolving, and we encourage contributions through pull requests to enhance its richness and comprehensiveness.
Featured Research Papers for 2024
Here are some significant papers that highlight the ongoing research in GEO:
- GEO: Generative Engine Optimization
- What Evidence Do Language Models Find Convincing?
- CONFLICTBANK: A Benchmark for Evaluating Knowledge Conflicts in Large Language Models
- Adversarial Search Engine Optimization for Large Language Models
- Ranking Manipulation for Conversational Search Engines
- PERSISTENT PRE-TRAINING POISONING OF LLMS
Explore the world of Generative Engine Optimization with us and contribute to reshaping the future of content visibility!