arXiv:2605.26400v1 Announce Type: cross Abstract: We propose a framework for evaluating structured generative search summaries that are placed atop organic web search results. A structured summary, generated by a large language model, typically consists of an overview, several sections with section titles, and a list of source documents that are cited within the summary. We then describe our plans for implementing and evaluating the framework.
The proliferation of generative AI necessitates robust evaluation frameworks to ensure quality and reliability, particularly as these technologies are integrated into critical information retrieval systems.
A strategic reader should care because the methodology for evaluating generative search summaries directly impacts the trustworthiness, utility, and user adoption of AI-powered information systems, influencing public discourse and enterprise operations.
The focus is shifting from basic generative AI output to structured, verifiable, and source-cited summaries, directly addressing concerns about AI hallucinations and reliability in information retrieval.
- · AI evaluation framework developers
- · Search engine providers
- · Information retrieval researchers
- · Users seeking reliable information
- · Generative AI models producing unverified summaries
- · Platforms lacking robust evaluation capabilities
Improved quality and trustworthiness of AI-generated search summaries integrated into web search results.
Increased user reliance on AI-generated summaries, potentially altering traditional web browsing patterns and click-through rates to original sources.
The development of industry standards for 'structured generative summaries' could emerge, creating a new benchmark for AI information services.
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Read at arXiv cs.AI