What is Neural Retrieval?
A retrieval method that uses deep learning models to generate embeddings and match queries with documents.
More about Neural Retrieval:
Neural Retrieval leverages deep learning models to generate embeddings for both queries and documents, matching them based on semantic similarity. This approach is more effective than traditional retrieval methods at capturing nuanced meanings, making it ideal for tasks like dense retrieval and semantic search.
Neural retrieval is widely adopted in retrieval augmentation pipelines and applications like question answering, where relevance and accuracy are critical.
Frequently Asked Questions
What are the benefits of neural retrieval?
It captures semantic relationships more effectively than traditional methods, improving accuracy and relevance.
What tools and models are commonly used for neural retrieval?
Tools like vector databases and models like BERT, sentence transformers, and GPT are frequently used in neural retrieval systems.
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