Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization (2504.01018v1)

Abstract: Selective retrieval improves retrieval-augmented generation (RAG) by reducing distractions from low-quality retrievals and improving efficiency. However, existing approaches under-utilize the inherent knowledge of LLMs, leading to suboptimal retrieval decisions and degraded generation performance. To bridge this gap, we propose Self-Routing RAG (SR-RAG), a novel framework that binds selective retrieval with knowledge verbalization. SR-RAG enables an LLM to dynamically decide between external retrieval and verbalizing its own parametric knowledge. To this end, we design a multi-task objective that jointly optimizes an LLM on knowledge source selection, knowledge verbalization, and response generation. We further introduce dynamic knowledge source inference via nearest neighbor search to improve the accuracy of knowledge source decision under domain shifts. Fine-tuning three LLMs with SR-RAG significantly improves both their response accuracy and inference latency. Compared to the strongest selective retrieval baseline, SR-RAG reduces retrievals by 29% while improving the performance by 5.1%.

An Analysis of Self-Routing RAG: Advancement in Retrieval-Augmented Generation

The paper "Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalization" proposes an advanced framework termed Self-Routing RAG (SR-RAG) that seeks to improve on the existing paradigms of retrieval-augmented generation (RAG) by integrating selective retrieval with knowledge verbalization, specifically enhancing the utilization of LLMs.

Core Objective

The primary aim of the SR-RAG framework is to enable LLMs to dynamically decide whether to engage external retrieval systems or leverage their intrinsic parametric knowledge through verbalization. This decision-making process is facilitated through a multi-task learning objective which simultaneously optimizes the LLM across three dimensions: knowledge source selection, knowledge verbalization, and response generation, promising both improved accuracy in responses and reduced latency during inference.

Methodological Innovation

SR-RAG introduces several key innovations to address the challenges associated with retrieval augmented generation:

1. Knowledge Source Selection: SR-RAG implements a mechanism that allows for the dynamic decision making between external retrieval and self-knowledge verbalization. This is achieved through nearest-neighbor search that helps in accurately deciding the knowledge source under domain shifts.
2. Multi-task Alignment Objective: This novel objective aligns the tasks of source selection, verbalization, and response generation, fostering a deeper intra-model collaboration to leverage the LLM’s full capabilities without reliance on external retrieval.
3. Dynamic Knowledge Source Inference: Implementing nearest neighbor-enhanced inference strategies to overcome accuracy issues associated with domain shifts, emphasizing efficient task-specific knowledge retrieval and generation practices.

Experimental Evaluation

The paper conducts extensive experimental evaluations to validate the efficacy of SR-RAG. Tests involving fine-tuning of three distinct LLMs demonstrate a considerable improvement in accuracy and inference efficiency. Key results include an average performance enhancement of 8.5% over the strongest baseline approach with a reduction in retrieval occurrences by 26% to 40% across different LLMs and benchmark datasets. This highlights SR-RAG’s potential in reducing unnecessary computational costs while enhancing performance metrics.

Implications and Future Prospects

The theoretical implications of this research point towards a significant paradigm shift in how retrieval augmented systems might operate, leveraging internally stored knowledge in LLMs more effectively. Practically, this framework offers a pathway towards efficient, scalable RAG systems that better manage computational resources and deliver effective real-time results in knowledge-intensive tasks.

For future developments, there is potential exploration in expanding the variety and granularity of knowledge sources within SR-RAG, potentially creating more nuanced decision-making processes as well as improving the ability of models to adapt to dynamic, real-world data shifts. The scaffold laid by SR-RAG may allow for greater integration across various domains, reinforcing the role of LLMs in intelligent decision-making systems.

Conclusion

The robust methodology and promising results presented in this paper indicate that SR-RAG represents a substantial advancement in the field of retrieval-augmented generation, moving towards a more resource-efficient and responsive framework. By binding selective retrieval with knowledge verbalization, SR-RAG maximizes the potential of LLMs, proposing a forward-looking strategy that can be refined and expanded in the field of AI research.