FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG (2410.10293v1)

Abstract: Retrieval-Augmented Generation (RAG) prevails in LLMs. It mainly consists of retrieval and generation. The retrieval modules (a.k.a. retrievers) aim to find useful information used to facilitate generation modules (a.k.a. generators). As such, generators' performance largely depends on the effectiveness and efficiency of retrievers. However, the retrieval paradigm that we design and use remains flat, which treats the retrieval procedures as a one-off deal with constant granularity. Despite effectiveness, we argue that they suffer from two limitations: (1) flat retrieval exerts a significant burden on one retriever; (2) constant granularity limits the ceiling of retrieval performance. In this work, we propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency. Specifically, FunnelRAG establishes a progressive retrieval pipeline by collaborating coarse-to-fine granularity, large-to-small quantity, and low-to-high capacity, which can relieve the burden on one retriever and also promote the ceiling of retrieval performance. Extensive experiments manifest that FunnelRAG achieves comparable retrieval performance while the time overhead is reduced by nearly 40 percent.

An Analysis of FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG

The paper "FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG" explores the intricacies of improving retrieval-augmented generation (RAG) in LLMs by introducing a novel progressive retrieval approach. RAG is pivotal in supplementing LLMs with non-parametric external knowledge to counteract their informational inadequacies or historical inaccuracies. However, traditional retrieval methods within RAG frameworks often adopt a flat retrieval scheme, impairing the overall performance due to the burden placed on singular retrieval implements and the constant granularity approach. FunnelRAG proposes to overcome these limitations through a unique coarse-to-fine retrieval progression, enhancing the enforcement of retrieval tasks by diversifying granularity and capacity.

Key Methodologies and Contributions

The core innovation within FunnelRAG is its three-stage retrieval pipeline, which transitions from coarse-grained clustering of documents to fine-grained passage-level retrieval. This technique mirrors the efficiency of funneling comprehensive data into precision-focused segments. The process is as follows:

1. Retrieval Stage: Initiates with a coarse-grained document clustering strategy using sparse retrievers, such as BM25, aiming at capturing maximal answer recall without considering unit granularity. This approach fosters an initial filtering layer within a significantly large corpus.
2. Pre-Ranking Stage: Utilizes cross-encoder models to refine the coarse units into document-level granularity. This stage is crucial as it transforms the vast retrieval corpus into a concentrated selection by efficient sparse retriever application, subsequently alleviating burden from the initial massive candidate pool.
3. Post-Ranking Stage: Concludes with a focus on passage-level granularity through models like FiD, which are adept at aligning the retrieval output with model preferences. This finale fosters the highest precision by amalgamating dense retrieval and ranking knowledge aggregation.

The introduction of local-to-global (L2G) distillation techniques embodies a cornerstone contribution of FunnelRAG. This method bridges the granularity gap between sequential retrieval stages, allowing for an aggregated scoring system that enhances retriever synchronization and model performance coherence.

Empirical Results and Implications

The empirical outcomes, demonstrated on open-domain QA datasets such as Natural Questions (NQ) and Trivia QA (TQA), exhibit FunnelRAG's capability to achieve retrieval performance at par or exceeding traditional flat retrieval schemes, while significantly reducing computational time. This reduction is quantified at nearly 40%, which is substantial in practical applications where resource efficiency is pivotal. Such results testify to the robustness and tactical efficiency of the progressive retrieval paradigm, suggesting broader applicability across diverse retrieval-augmented tasks in AI.

Broader Impact and Future Directions

The implications of FunnelRAG resonate beyond the immediate performance benefits observed within the confines of QA tasks. Its methodology encourages a paradigm shift in retrieval strategies by underscoring the efficacy of leveraging multi-layered retrieval progression. As AI continues exploring the integration of LLMs with retrieval systems for varied applications, FunnelRAG presents a model wherein retrieval can adapt progressively, hence optimizing both retrieval unit integrity and response coherence.

Future research may focus on further optimizing the balance between retrieval granularity and retriever capacity, potentially exploring hybrid approaches incorporating real-time adjustments based on task requirements or model iterations. Additionally, expanding FunnelRAG's applicability to other domains or more complex RAG frameworks could amplify its utility and encourage new methodologies derived from its core principles.

In conclusion, the FunnelRAG framework offers a coherent, progressive retrieval paradigm that transcends traditional limitations, introducing a systematic approach that balances both the retrieval efficacy and computational efficiency necessary for contemporary AI challenges.