Dense Retrieval (DR) Models in Information Retrieval
Practical Solutions and Value
Dense Retrieval (DR) models use deep learning techniques to map passages and queries into an embedding space, determining semantic relationships and balancing effectiveness and efficiency.
PLMs and Transformer Architecture
Practical Solutions and Value
Pre-trained language models (PLMs) based on the Transformer architecture are effective for query and passage encoding, capturing complex semantic linkages and dependencies over lengthy text sequences.
Challenges and Non-Transformer PLMs
Practical Solutions and Value
Non-transformer PLMs, such as the Mamba architecture, offer improved processing speed and comparable effectiveness, especially for long-text retrieval tasks, addressing the computational cost challenges of Transformer-based models.
Mamba Retriever Model
Practical Solutions and Value
The Mamba Retriever is designed to maximize efficiency and effectiveness in information retrieval operations, showing superior performance in long-text retrieval tasks and providing fast inference speed.
AI Solutions for Business
Practical Solutions and Value
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AI for Sales Processes and Customer Engagement
Practical Solutions and Value
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