AI Search Engine Reliability Evaluation

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TechXplore

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Team introduces a cost-effective method to redesign search engines for AI

Summary

Nutrition label

89% Informative

Computer scientists at UMass Amherst have developed a new system for evaluating the reliability of AI -generated searches.

Called "eRAG," the method is a way of putting the AI and search engine in conversation with each other.

The work is published as part of the Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval .

The accuracy, cost-effectiveness and ease with which eRAG can be implemented is a major step toward the day when all our search engines run on AI .

This research has been awarded a Best Short Paper Award by the Association for Computing Machinery's International Conference on Research and Development in Information Retrieval (SIGIR 2024).

VR Score

Informative language

Neutral language

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formal

Language

English

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not offensive

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not hateful