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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
92
Informative language
93
Neutral language
68
Article tone
formal
Language
English
Language complexity
58
Offensive language
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Hate speech
not hateful
Attention-grabbing headline
not detected
Known propaganda techniques
not detected
Time-value
long-living
External references
5
Source diversity
5
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