The field of information retrieval is undergoing a significant shift with the rapid adoption of generative AI-powered search engines. This shift is challenging traditional Search Engine Optimization (SEO) practices and necessitating a new paradigm. Recent research has focused on understanding the differences between AI Search and traditional web search, as well as developing new strategies for achieving visibility in the new generative search landscape.

A key area of innovation is the use of large language models (LLMs) to improve search results and provide more accurate information retrieval. LLMs are being used to analyze and understand the context of search queries, as well as to generate more informative and relevant search results.

Another important area of research is the development of new methods for classifying and organizing information, such as patent-SDG classification. This involves using LLMs to extract structured concepts from patents and SDG papers, and then using these concepts to classify patents according to their relevance to the UN Sustainable Development Goals.

Noteworthy papers in this area include: Generative Engine Optimization: How to Dominate AI Search, which presents a comprehensive comparative analysis of AI Search and traditional web search, and provides actionable guidance for practitioners. From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models, which develops a composite labeling function that uses LLMs to extract structured concepts from patents and SDG papers. When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine, which investigates the distinctive characteristics of generative search engines and their impact on search results.