What is Query Expansion & Cross Order re-ranking in RAG

 In Retrieval-Augmented Generation (RAG) models, query expansion and cross-order re-ranking work together to improve the quality of documents retrieved for a user's query and ultimately, the response generated by the large language model (LLM). Here's a breakdown of each step:

1. Query Expansion:

This stage aims to broaden the scope of the user's initial query.

The RAG model uses techniques like word embeddings or synonym identification to find related words or phrases that capture the same or similar meaning as the original query.

By including these expanded terms, the model can potentially retrieve a wider range of relevant documents from the document store.

Benefits:

Increased Recall: Query expansion helps capture documents that might not contain the exact keywords from the user's query but are still relevant due to semantic similarity.

Improved Context: Including related terms can provide the LLM with a richer context for understanding the user's intent.

2. Cross-order Re-ranking:

After the retrieval stage (where documents are initially found based on the original or expanded query), cross-order re-ranking comes into play.

Here, the RAG model employs a different technique, often a cross-encoder. This is a neural network architecture trained to compare a document and a query and determine their relevance.

The cross-encoder analyzes the retrieved documents and the user's query (potentially including the expanded terms). Based on this analysis, it re-ranks the documents, placing the most relevant ones at the top.

Benefits:

Improved Precision: Re-ranking helps ensure that the most pertinent documents are prioritized, even if they weren't initially retrieved at the top positions during the initial retrieval stage.

Enhanced Relevance: By using a semantic understanding of the query and documents, the cross-encoder can identify subtle connections and rank documents that best address the user's information needs.

Overall Impact:

By combining query expansion and cross-order re-ranking, RAG models achieve a more effective document retrieval process. The expanded query helps capture a wider range of relevant documents, while the cross-encoder refines the selection, ensuring the most pertinent information reaches the LLM for response generation. This leads to a more accurate and informative answer for the user.

references:

Gemini