What is Dense Retrieval?
A retrieval method that uses dense vector embeddings, enabling semantic search and advanced contextual retrieval.
More about Dense Retrieval:
Dense Retrieval uses dense vector embeddings to match queries with documents based on semantic similarity, rather than relying on exact term matching. Models like bi-encoders and cross-encoders are often employed to create these embeddings.
Dense retrieval is a key component in systems like RAG and semantic search, providing superior accuracy in understanding user intent and delivering relevant results.
Frequently Asked Questions
What are the advantages of dense retrieval over sparse retrieval?
Dense retrieval captures semantic relationships between words, making it ideal for tasks like contextual retrieval.
What tools or models are commonly used for dense retrieval?
Popular tools include vector databases, powered by models like BERT and RoBERTa.
From the blog
Fine-tuning your custom ChatGPT chatbot
Finetuning your custom chatbot is a crucial step in ensuring that it can answer your visitors questions correctly and with the best possible information.
Herman Schutte
Founder
IT Help Desk Automation with SiteSpeakAI
In a world that’s constantly evolving, having a robust IT help desk is no longer a choice but a necessity for businesses. But, how can you ensure that your help desk is able to respond to queries swiftly and accurately? The answer lies in automation, and one tool that is making waves in this domain is SiteSpeakAI.
Herman Schutte
Founder