HyDE to Improve your RAG (with one line of code!)

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HyDE (Hypothetical Document Embeddings)

Link to article: 2212.10496 (arxiv.org)

HyDE Rag stands out for its unique approach that leverages the capabilities of both neural language models and contrastive encoders without requiring explicit relevance judgments.

Understanding HyDE Rag: A Novel Approach

The Problem with Traditional Dense Retrieval

Dense retrieval models typically measure the similarity between queries and documents using inner product similarity. These models rely on two encoder functions — one for the query and one for the document — mapping them into vectors whose inner product indicates relevance. This process, however, usually requires a large collection of relevance judgments (i.e., labeled data indicating which documents are relevant to which queries) to train the encoders effectively.

Introducing HyDE Rag

HyDE (Hypothetical Document Embedding) Rag sidesteps the need for these relevance judgments by transforming the retrieval task into two sub-tasks: Natural Language Understanding (NLU) and Natural Language Generation (NLG). Here’s how it works:

  1. Generative Query Transformation: Instead of directly encoding the query, HyDE uses an instruction-following language…

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