Innovative AI Solution for Low-Latency Information Retrieval
In today’s digital world, instant gratification is paramount. People expect fast and accurate responses to their queries, but traditional search engines face challenges in achieving both speed and precision.
Shallow Cross-Encoders: AI Revolutionizing Search
Researchers at the University of Glasgow have developed shallow Cross-Encoders, a novel AI approach that balances speed and accuracy. These compact transformer models deliver lightning-fast retrieval without compromising precision.
Efficiency and Effectiveness
Shallow Cross-Encoders, such as TinyBERT, MiniBERT, and SmallBERT, outperform larger models in low-latency scenarios. For example, TinyBERT-gBCE achieved a 51% improvement in NDCG@10 score at a latency of just 25 milliseconds compared to larger models like MonoBERT-Large.
Energy Efficiency and Cost-Effectiveness
These compact models offer significant benefits in terms of energy efficiency and cost-effectiveness. They can be deployed on various devices without requiring specialized hardware acceleration.
Practical AI Solutions
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Researchers at the University of Glasgow Propose Shallow Cross-Encoders as an AI-based Solution for Low-Latency Information Retrieval
- MarkTechPost
- Twitter – @itinaicom
