Unifying Context Ranking and Retrieval-Augmented Generation with RankRAG

Large language models (LLMs) have become increasingly powerful tools for tackling a wide range of knowledge-intensive natural language processing (NLP) tasks. One such technique that has gained significant attention is retrieval-augmented generation (RAG), which combines the strengths of LLMs with the ability to retrieve relevant information from external sources.

In the standard RAG pipeline, a standalone retriever first extracts the top-k most relevant contexts from a large corpus, which are then fed into the LLM to generate the final answer. While this approach has shown promising results, it faces several limitations that can hinder its performance.

Limitations of top-k chunks approach

One key challenge is the trade-off in selecting the optimal number of retrieved contexts (k). A smaller k may fail to capture all the relevant information, compromising the recall, while a larger k can introduce irrelevant content that hampers the LLM’s ability to generate accurate answers. Additionally, the limited capacity of the retriever, often a moderate-sized model, can constrain its effectiveness in accurately matching the question to the relevant documents, especially in new tasks or domains.

RankRAG

To address these limitations, researchers at NVIDIA have proposed a novel framework called RankRAG, which instruction-tunes a single LLM for both context ranking and…