Reranking: RAG-Fusion

Suppose we’re searching for information online. We type in our query, and the system returns a list of results. But are they truly the most relevant? Traditional ranking algorithms often prioritize factors like keyword matching, which can miss the deeper meaning of your search. This is where reranking comes in.

What is reranking?

Reranking is a two-stage retrieval process that improves the relevance of search results. Here’s how it works:

• Initial retrieval: A primary system, like a search engine, retrieves a large pool of potentially relevant items based on keywords or other factors.

• Refining the list: A reranking model, often powered by machine learning, analyzes each item in the pool and assigns a new score based on its true relevance to the user’s query. This score can consider factors like semantic similarity and user context.

• Reordered results: Finally, the items are reordered based on their new scores, presenting the most relevant results at the top.

Types of reranking techniques

Several innovative techniques can be employed for reranking. Let’s explore two prominent approaches:

• RAG-Fusion (Retrieval-Augmented Generation Fusion): This technique combines two models: A retriever that finds potentially relevant documents and a generative model that understands the query’s intent. RAG-Fusion leverages the strengths of both, often using a reranker to improve the final selection of documents for the generative model to process.

• Cross-Encoder Reranking: Here, a separate model called a cross-encoder takes the query and each retrieved item as input. It then outputs a score indicating how well the item matches the user’s intent. This score reranks the initial list and presents the most semantically similar items at the top.

What is RAG-Fusion?

RAG-Fusion combines retrieval (finding relevant documents) with generation (formulating queries). It leverages an LLM to create these query variations based on the user’s original question. Using an LLM, RAG-Fusion can capture the nuances of language and generate queries that effectively represent the user’s intent.

RAG-Fusion is a technique that builds on top of RAG models to improve search results, particularly in the context of chatbots. Here’s a breakdown of how it works:

• Understanding the user’s intent: RAG-Fusion starts with a user query. Like RAG models, it aims to understand the true intent behind the question.

• Generating multiple queries: RAG-Fusion goes beyond a single query. It uses the original query to create multiple variations, essentially rephrasing the question from different angles. This helps capture the nuances of the user’s intent.

• Retrieval with embedding: The original and generated queries are converted into a numerical representation using embedding models. This allows for efficient searching within a document collection or knowledge base. Documents relevant to each query are retrieved.

• Reciprocal rank fusion (RRF): RAG-Fusion then employs a reciprocal rank fusion (RRF) technique. RRF assigns scores based on how well-retrieved documents match each query. Documents with high scores across multiple queries will likely be more relevant to the user’s intent.

• Fusing documents and scores: Finally, RAG-Fusion combines the retrieved documents and their corresponding scores. This provides a richer set of information that can be used to formulate a response.