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https://arxiv.org/abs/2311.09735

[Skip to main content](https://arxiv.org/abs/2311.09735#content) Search arXiv Press Enter to search · [Advanced search](https://arxiv.org/search/advanced) # Computer Science > Machine Learning **arXiv:2311.09735** (cs) \[Submitted on 16 Nov 2023 ( [v1](https://arxiv.org/abs/2311.09735v1)), last revised 28 Jun 2024 (this version, v3)\] # Title:GEO: Generative Engine Optimization Authors: [Pranjal Aggarwal](https://arxiv.org/search/cs?searchtype=author&query=Aggarwal,+P), [Vishvak Murahari](https://arxiv.org/search/cs?searchtype=author&query=Murahari,+V), [Tanmay Rajpurohit](https://arxiv.org/search/cs?searchtype=author&query=Rajpurohit,+T), [Ashwin Kalyan](https://arxiv.org/search/cs?searchtype=author&query=Kalyan,+A), [Karthik Narasimhan](https://arxiv.org/search/cs?searchtype=author&query=Narasimhan,+K), [Ameet Deshpande](https://arxiv.org/search/cs?searchtype=author&query=Deshpande,+A) View a PDF of the paper titled GEO: Generative Engine Optimization, by Pranjal Aggarwal and Vishvak Murahari and Tanmay Rajpurohit and Ashwin Kalyan and Karthik Narasimhan and Ameet Deshpande [View PDF](https://arxiv.org/pdf/2311.09735) [HTML (experimental)](https://arxiv.org/html/2311.09735v3) > Abstract: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 user utility and generative 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 when and how 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. | | | | --- | --- | | Comments: | Accepted to KDD 2024 | | Subjects: | Machine Learning (cs.LG); Information Retrieval (cs.IR) | | Cite as: | [arXiv:2311.09735](https://arxiv.org/abs/2311.09735) \[cs.LG\] | | | (or [arXiv:2311.09735v3](https://arxiv.org/abs/2311.09735v3) \[cs.LG\] for this version) | | | [https://doi.org/10.48550/arXiv.2311.09735](https://doi.org/10.48550/arXiv.2311.09735)<br>Focus to learn more<br>arXiv-issued DOI via DataCite | ## Submission history From: Pranjal Aggarwal \[ [view email](https://arxiv.org/show-email/5b1a745d/2311.09735)\] **[\[v1\]](https://arxiv.org/abs/2311.09735v1)** Thu, 16 Nov 2023 10:06:09 UTC (2,081 KB) **[\[v2\]](https://arxiv.org/abs/2311.09735v2)** Tue, 28 May 2024 17:40:31 UTC (1,153 KB) **\[v3\]** Fri, 28 Jun 2024 17:59:26 UTC (751 KB) Full-text links: ## Access Paper: View a PDF of the paper titled GEO: Generative Engine Optimization, by Pranjal Aggarwal and Vishvak Murahari and Tanmay Rajpurohit and Ashwin Kalyan and Karthik Narasimhan and Ameet Deshpande - [View PDF](https://arxiv.org/pdf/2311.09735) - [HTML (experimental)](https://arxiv.org/html/2311.09735v3) - [TeX Source](https://arxiv.org/src/2311.09735) [view license](http://creativecommons.org/licenses/by/4.0/ "Rights to this article") ### Current browse context: cs.LG [< prev](https://arxiv.org/prevnext?id=2311.09735&function=prev&context=cs.LG "previous in cs.LG (accesskey p)") \| [next >](https://arxiv.org/prevnext?id=2311.09735&function=next&context=cs.LG "next in cs.LG (accesskey n)") [new](https://arxiv.org/list/cs.LG/new) \| [recent](https://arxiv.org/list/cs.LG/recent) \| [2023-11](https://arxiv.org/list/cs.LG/2023-11) Change to browse by: [cs](https://arxiv.org/abs/2311.09735?context=cs) [cs.IR](https://arxiv.org/abs/2311.09735?context=cs.IR) ### References & Citations - [NASA ADS](https://ui.adsabs.harvard.edu/abs/arXiv:2311.09735) - [Google Scholar](https://scholar.google.com/scholar_lookup?arxiv_id=2311.09735) - [Semantic Scholar](https://api.semanticscholar.org/arXiv:2311.09735) export BibTeX citation ### Bookmark [](http://www.bibsonomy.org/BibtexHandler?requTask=upload&url=https://arxiv.org/abs/2311.09735&description=GEO:%20Generative%20Engine%20Optimization "Bookmark on BibSonomy") [](https://reddit.com/submit?url=https://arxiv.org/abs/2311.09735&title=GEO:%20Generative%20Engine%20Optimization "Bookmark on Reddit") Bibliographic Tools # Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer _( [What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))_ Connected Papers Toggle Connected Papers _( [What is Connected Papers?](https://www.connectedpapers.com/about))_ Litmaps Toggle Litmaps _( [What is Litmaps?](https://www.litmaps.co/))_ scite.ai Toggle scite Smart Citations _( [What are Smart Citations?](https://www.scite.ai/))_ Code, Data, Media # Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv _( [What is alphaXiv?](https://alphaxiv.org/))_ Links to Code Toggle CatalyzeX Code Finder for Papers _( [What is CatalyzeX?](https://www.catalyzex.com/))_ DagsHub Toggle DagsHub _( [What is DagsHub?](https://dagshub.com/))_ GotitPub Toggle Gotit.pub _( [What is GotitPub?](http://gotit.pub/faq))_ Huggingface Toggle Hugging Face _( [What is Huggingface?](https://huggingface.co/huggingface))_ ScienceCast Toggle ScienceCast _( [What is ScienceCast?](https://sciencecast.org/welcome))_ Demos # Demos Replicate Toggle Replicate _( [What is Replicate?](https://replicate.com/docs/arxiv/about))_ Spaces Toggle Hugging Face Spaces _( [What is Spaces?](https://huggingface.co/docs/hub/spaces))_ Spaces Toggle TXYZ.AI _( [What is TXYZ.AI?](https://txyz.ai/))_ Related Papers # Recommenders and Search Tools Link to Influence Flower Influence Flower _( [What are Influence Flowers?](https://influencemap.cmlab.dev/))_ Core recommender toggle CORE Recommender _( [What is CORE?](https://core.ac.uk/services/recommender))_ IArxiv recommender toggle IArxiv Recommender _( [What is IArxiv?](https://iarxiv.org/about))_ - Author - Venue - Institution - Topic About arXivLabs # arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.

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