PrefRAG: Preference-Driven Multi-Source Retrieval Augmented Generation (2411.00689v2)

Abstract: Retrieval-Augmented Generation (RAG) has emerged as a reliable external knowledge augmentation technique to mitigate hallucination issues and parameterized knowledge limitations in LLMs. Existing adaptive RAG (ARAG) systems excel at in-depth exploration within a single source but struggle to effectively and controllably explore different retrieval sources, as they fail to foresee their internal knowledge features. We develop a novel multi-source ARAG system, PrefRAG, which enhances RAG by enabling in-depth and controllable exploration of diverse retrieval sources through preference-driven adaptive retrieval and self-reflection. PrefRAG first fully explores controllable local sources in adaptive retrieval and supplements with the web when appropriate, ultimately selecting the optimal source for knowledge observation. Subsequently, PrefRAG feeds answer quality feedback into the retrieval process, optimizing it from the generation perspective to produce higher-quality responses. Extensive experiments confirm its superiority, high retrieval efficiency, and knowledge controllability. PrefRAG outperforms Vanilla RAG and the leading MS-ARAG by up to 25.6% and 13.9% respectively. Additionally, PrefRAG trained with DPO achieves higher performance. The code and data are available at https://github.com/QingFei1/PrefRAG.git.

• The paper presents MSPR, a novel multi-source RAG framework that dynamically integrates reasoning with preference-driven retrieval to optimize information sourcing.
• It introduces an adaptive reasoning and retrieval agent along with a Preference-Driven Retrieval Strategy Selector, effectively choosing high-quality sources for multi-hop tasks.
• Experimental evaluations demonstrate up to 14.4% performance gains over standard methods, highlighting significant improvements in retrieval robustness and answer precision.

Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval

The paper authored by Zhao et al. addresses the challenges associated with Retrieval-Augmented Generation (RAG) systems, particularly focusing on the limitations of existing Adaptive RAG (ARAG) frameworks in effectively utilizing multiple retrieval sources. Their contribution, titled MSPR, aims to enhance RAG frameworks by synergizing reasoning and preference-driven retrieval, thereby advancing the process of deciding "when and what to retrieve" and "which retrieval source to use."

Core Contributions

1. Multi-Source ARAG Framework: MSPR represents a significant evolution from traditional RAG systems. While Vanilla RAG systems typically implement a one-time retrieval mechanism with a narrow scope, MSPR intelligently integrates multiple sources by dynamically selecting and utilizing them based on the needs of the task. This approach marks a pivotal advance as it ensures more comprehensive information retrieval and integration into LLMs.
2. Adaptive Reasoning and Retrieval: The paper introduces an adaptive reasoning-and-retrieval agent within MSPR. This agent drives dynamic adjustments of retrieval actions, featuring an iterative process that includes reasoning, action decision-making, and knowledge feedback observation. This process ensures that retrieval actions become more contextually aware and aligned with the question's requirements, enhancing the robustness of the retrieval phase.
3. Preference-Driven Retrieval Strategy Selector (PRS): A novel component of the framework, PRS, guides the agent in navigating the retrieval space. It prioritizes leveraging high-quality primary sources and supplements them with secondary sources based on strategic necessity. This component is crucial for avoiding common pitfalls associated with arbitrary and non-optimal source selection.
4. Corrective Answer Reviewer (CAR): To address issues of output quality, MSPR employs CAR, a feedback mechanism that evaluates generated answers. This mechanism instructs the system to initiate supplementary retrieval actions when necessary, refining the final output's accuracy and completeness.

Experimental Evaluation

Extensive experimental evaluation on three complex datasets — HotpotQA, 2WikiMultiHopQA, and MuSiQue — underscores MSPR's superior performance. The framework outperformed notable baselines, including standard Vanilla RAG systems, existing ARAG methods like Self-RAG and FLARE, and multi-source approaches such as CRAG and ReAct, achieving up to 14.4% improvement on certain metrics. The ablation studies further highlight the critical role of PRS and CAR in enhancing the framework's efficacy.

Practical and Theoretical Implications

Practically, MSPR's dynamic retrieval framework offers significant improvements in tasks requiring comprehensive knowledge integration, such as multi-hop question answering, where a deep reasoning chain is essential. Theoretically, the paper advances our understanding of multi-source integration in LLMs, providing a robust foundation for future research into adaptive retrieval systems.

Future Directions

Future research could explore further optimization of retrieval source selection criteria, potentially integrating machine learning models to predict source utility in real-time. Moreover, extending MSPR's capabilities to a broader range of tasks, including conversational agents and complex decision-making systems, could prove beneficial. Investigating the scalability of MSPR with ever-increasing external knowledge bases would also be a critical trajectory for future inquiry.

In summary, Zhao et al.'s MSPR framework provides a compelling and technically sound advancement for retrieval-augmented LLMs, emphasizing the importance of adaptive and multi-source retrieval strategies. The framework offers a well-founded basis for ongoing research aiming to mitigate hallucination issues in LLMs and foster the development of more accurate and reliable AI systems.