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

A paper proposing a multi-objective multi-armed bandit enhanced RAG framework for knowledge graphs.

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

By Xiaqiang Tang, Jian Li, Nan Du, Sihong XieProceedings of the AAAI Conference on Artificial Intelligence
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The authors introduce a framework that adapts to non-stationary environments by selecting the most suitable retrieval method based on user feedback and historical performance.

This approach is applied to Retrieval-Augmented Generation (RAG) on knowledge graphs, aiming to enhance reasoning capabilities of large language models. Experiments demonstrate improved performance in both stationary and non-stationary settings.

Abstract

The authors introduce a framework that adapts to non-stationary environments by selecting the most suitable retrieval method based on user feedback and historical performance. This approach is applied to Retrieval-Augmented Generation (RAG) on knowledge graphs, aiming to enhance reasoning capabilities of large language models. Experiments demonstrate improved performance in both stationary and non-stationary settings.

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multi-armed banditnon-stationary environmentsretrieval-augmented generationknowledge graphslarge language modelsadversarial learningKnowledge GraphsRetrieval & RAGLarge Language ModelsAI Agents
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Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs | Aramai