RAG
Save
Retrieval Augmented Generation (RAG) is a cutting-edge technique that enhances the capabilities of large language models (LLMs). By leveraging a dual-encoder architecture, RAG empowers LLMs with the ability to retrieve relevant information from extensive text databases, significantly improving their performance on various text-based tasks.
Understanding RAG
The core concept of RAG lies in its dual-encoder structure, consisting of a text encoder and a retrieval encoder. The text encoder converts the input text into a fixed-length representation, while the retrieval encoder transforms the text database into a collection of vectors.
To perform retrieval, the text encoder generates a query vector for the input text, which is then compared to the vectors in the database. The retrieval encoder identifies the most similar vectors, corresponding to the most relevant documents from the database.
Benefits of RAG
RAG offers several advantages that enhance the performance of LLMs:
- Improved Information Retrieval: RAG enables LLMs to access and leverage external knowledge, improving their ability to find and retrieve relevant information.
- Enhanced Text Generation: By incorporating retrieved information, LLMs can generate more informed and contextually rich text.
- Better Question Answering: RAG empowers LLMs to provide comprehensive and accurate answers to complex questions, utilizing external knowledge.
- Augmented Summarization: RAG helps LLMs generate concise and informative summaries of large text corpora, leveraging retrieved information.
