# GEO: Generative Engine Optimization

arXiv: https://arxiv.org/abs/2311.09735 · HTML: https://mlsanity.com/paper/2311.09735/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Teaching language models to support answers with verified quotes](https://mlsanity.com/paper/2203.11147/) (2022)
- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://mlsanity.com/paper/2208.03188/) (2022)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval](https://mlsanity.com/paper/2107.11976/) (2021)
- [MS MARCO: Benchmarking Ranking Models in the Large-Data Regime](https://mlsanity.com/paper/2105.04021/) (2021)
- [ORCAS-I: Queries Annotated with Intent using Weak Supervision](https://mlsanity.com/paper/2205.00926/) (2022)

## Built on by (most cited first, 30 of 56)

- [C-SEO Bench: Does Conversational SEO Work?](https://mlsanity.com/paper/2506.11097/) (2025)
- [Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines](https://mlsanity.com/paper/2501.00745/) (2025)
- [SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization](https://mlsanity.com/paper/2602.12187/) (2026)
- [Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior](https://mlsanity.com/paper/2603.29979/) (2026)
- [Deep-Research Agents Can Be Poisoned via User-Generated Content](https://mlsanity.com/paper/2605.24245/) (2026)
- [IF-GEO: Conflict-Aware Instruction Fusion for Multi-Query Generative Engine Optimization](https://mlsanity.com/paper/2601.13938/) (2026)
- [Controlling Output Rankings in Generative Engines for LLM-based Search](https://mlsanity.com/paper/2602.03608/) (2026)
- [Diagnosing and Repairing Citation Failures in Generative Engine Optimization](https://mlsanity.com/paper/2603.09296/) (2026)
- [SCI-Defense: Defending Manipulation Attacks from Generative Engine Optimization](https://mlsanity.com/paper/2605.21948/) (2026)
- [Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines](https://mlsanity.com/paper/2606.20065/) (2026)
- [Exposing Citation Vulnerabilities in Generative Engines](https://mlsanity.com/paper/2510.06823/) (2025)
- [Caption Injection for Optimization in Generative Search Engine](https://mlsanity.com/paper/2511.04080/) (2025)
- [How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews](https://mlsanity.com/paper/2604.27790/) (2026)
- [How LLMs Are Persuaded: A Few Attention Heads, Rerouted](https://mlsanity.com/paper/2605.09314/) (2026)
- [What Gets Cited: Competitive GEO in AI Answer Engines](https://mlsanity.com/paper/2605.25517/) (2026)
- [Paraphrase Brittleness in Production Retrieval-Augmented Commercial Recommendation: Reproducibility Below the Rerun-Stability Baseline](https://mlsanity.com/paper/2605.27440/) (2026)
- [GEO-Flag: Detecting and Measuring GEO-Optimized Web Content](https://mlsanity.com/paper/2608.16824/) (2026)
- [CSP: A Simulator For Multi-Agent Ranking Competitions](https://mlsanity.com/paper/2502.11197/) (2025)
- [AI Answer Engine Citation Behavior An Empirical Analysis of the GEO16 Framework](https://mlsanity.com/paper/2509.10762/) (2025)
- [MillStone: How Open-Minded Are LLMs?](https://mlsanity.com/paper/2509.11967/) (2025)
- [LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing](https://mlsanity.com/paper/2510.01622/) (2025)
- [Toward Human-Centered AI-Assisted Terminology Work](https://mlsanity.com/paper/2512.18859/) (2025)
- [Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement](https://mlsanity.com/paper/2603.08924/) (2026)
- [The End of Rented Discovery: How AI Search Redistributes Power Between Hotels and Intermediaries](https://mlsanity.com/paper/2603.20062/) (2026)
- [Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact](https://mlsanity.com/paper/2605.14021/) (2026)
- [Generative AI Advertising as a Problem of Trustworthy Commercial Intervention](https://mlsanity.com/paper/2605.18673/) (2026)
- [Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit](https://mlsanity.com/paper/2605.27439/) (2026)
- [Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit](https://mlsanity.com/paper/2605.30207/) (2026)
- [Disentangling Answer Engine Optimization from Platform Growth: A Log-Based Natural Experiment on ChatGPT Referral Traffic](https://mlsanity.com/paper/2606.04362/) (2026)
- [Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection](https://mlsanity.com/paper/2606.16344/) (2026)

## Abstract (arXiv)

The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. This emerging technology, which we formalize under the unified framework of generative engines (GEs), can generate accurate and personalized responses, rapidly replacing traditional search engines like Google and Bing. Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them using LLMs. While this shift significantly improves textituser utility and textitgenerative search engine traffic, it poses a huge challenge for the third stakeholder -- website and content creators. Given the black-box and fast-moving nature of generative engines, content creators have little to no control over textitwhen and textithow their content is displayed. With generative engines here to stay, we must ensure the creator economy is not disadvantaged. To address this, we introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses through a flexible black-box optimization framework for optimizing and defining visibility metrics. We facilitate systematic evaluation by introducing GEO-bench, a large-scale benchmark of diverse user queries across multiple domains, along with relevant web sources to answer these queries. Through rigorous evaluation, we demonstrate that GEO can boost visibility by up to 40% in generative engine responses. Moreover, we show the efficacy of these strategies varies across domains, underscoring the need for domain-specific optimization methods. Our work opens a new frontier in information discovery systems, with profound implications for both developers of generative engines and content creators.