Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs (2412.07618v2)

Abstract: Despite the superior performance of LLMs on many NLP tasks, they still face significant limitations in memorizing extensive world knowledge. Recent studies have demonstrated that leveraging the Retrieval-Augmented Generation (RAG) framework, combined with Knowledge Graphs that encapsulate extensive factual data in a structured format, robustly enhances the reasoning capabilities of LLMs. However, deploying such systems in real-world scenarios presents challenges: the continuous evolution of non-stationary environments may lead to performance degradation and user satisfaction requires a careful balance of performance and responsiveness. To address these challenges, we introduce a Multi-objective Multi-Armed Bandit enhanced RAG framework, supported by multiple retrieval methods with diverse capabilities under rich and evolving retrieval contexts in practice. Within this framework, each retrieval method is treated as a distinct ``arm''. The system utilizes real-time user feedback to adapt to dynamic environments, by selecting the appropriate retrieval method based on input queries and the historical multi-objective performance of each arm. Extensive experiments conducted on two benchmark KGQA datasets demonstrate that our method significantly outperforms baseline methods in non-stationary settings while achieving state-of-the-art performance in stationary environments. Code and data are available at https://github.com/FUTUREEEEEE/Dynamic-RAG.git

• The paper introduces a novel MAB-enhanced RAG framework that dynamically selects retrieval methods based on historical performance to adapt to non-stationary environments on knowledge graphs.
• It employs a Multi-objective Multi-Armed Bandit (MO-MAB) using the Generalized Gini Index (GGI) to balance multiple performance metrics like coverage, reasoning, and response time.
• Experimental validation shows the proposed framework significantly outperforms baselines in non-stationary settings, achieving improvements up to 2% in hit rate and over 2.5% in recall on standard datasets while reducing retrieval delay.

Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs

The presented paper addresses the substantial challenge faced by Retrieval-Augmented Generation (RAG) systems in adapting to non-stationary environments, specifically through the integration of Multi-Armed Bandit (MAB) frameworks with Knowledge Graphs (KGs). While RAG systems have shown efficacy in improving the reasoning capabilities of LLMs by incorporating world knowledge stored in KGs, their ability to maintain performance in dynamic environments remains limited. This paper proposes a novel approach utilizing a Multi-objective Multi-Armed Bandit (MO-MAB) to enhance RAG implementations, particularly in non-stationary scenarios, ensuring optimal retrieval method selection and continuous adaptation using real-time feedback.

Core Contributions

1. MAB-Enhanced Framework: The paper introduces an innovative approach where multiple retrieval methods, each representing an arm within the MAB framework, are dynamically selected based on input queries and cumulative historical performance. This adaptation allows RAG systems to maintain robustness in dynamic, evolving environments by leveraging a flexible retrieval strategy.
2. Multi-Objective Optimization: The adoption of the Generalized Gini Index (GGI) for multi-objective reward aggregation ensures a balanced optimization of multiple performance metrics, such as retrieval coverage, reasoning power, and response time. This balance is crucial for delivering timely and informative responses under varying real-world conditions.
3. Experimental Validation: The paper provides extensive empirical evidence, with evaluations on two well-established KGQA datasets. The results indicate that the proposed MAB-enhanced RAG framework significantly outperforms existing baseline methods in non-stationary environments and achieves state-of-the-art performance in stationary environments.

Numerical Findings and Analysis

The paper's experimental setup highlights the advantages of the proposed approach in various scenarios. The MAB-enhanced RAG framework consistently showed superior performance across key metrics. For instance, on the ComplexWebQuestions dataset, the system achieved an enhancement close to 2% in hit rate and over 2.5% in recall compared to the nearest competing method. These results demonstrate the framework's capacity to efficiently adjust retrieval strategies in response to dynamic user input patterns and system changes.

Furthermore, in non-stationary settings, the system exhibited impressive adaptability with a notably reduced retrieval delay. This indicates substantial improvement in system responsiveness and user experience, underscoring the practical benefits of integrating MO-MAB to manage real-world application complexities.

Implications and Future Directions

The paper’s contributions have significant implications for the deployment and evolution of RAG systems in real-world applications, such as personal assistants and customer service bots, which frequently encounter non-stationary environments. The demonstrated ability to dynamically optimize retrieval methods based on evolving operational conditions holds potential for more resilient and user-satisfying AI systems.

Looking ahead, future research could expand on incorporating more nuanced user feedback mechanisms to further enhance adaptive capabilities. Additionally, exploring the integration of other advanced retrieval methods and continuously improving MAB algorithms could yield further efficiency gains and broader application adaptability. The comprehensive application of this research may ultimately influence the design of robust, intelligent systems capable of managing complex, dynamic information landscapes across various industry applications.