MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity

• URL: http://arxiv.org/abs/2412.01572v4
• Date: Wed, 01 Jan 2025 08:52:20 GMT
• Title: MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity
• Authors: Xiaqiang Tang, Qiang Gao, Jian Li, Nan Du, Qi Li, Sihong Xie,
• Abstract summary: We propose a reinforcement learning-based framework that dynamically selects the most suitable retrieval strategy based on query complexity.<n>Our method achieves new state of the art results on multiple single-hop and multi-hop datasets while reducing retrieval costs.
• Score: 30.346398341996476
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrieval Augmented Generation (RAG) has proven to be highly effective in boosting the generative performance of language model in knowledge-intensive tasks. However, existing RAG framework either indiscriminately perform retrieval or rely on rigid single-class classifiers to select retrieval methods, leading to inefficiencies and suboptimal performance across queries of varying complexity. To address these challenges, we propose a reinforcement learning-based framework that dynamically selects the most suitable retrieval strategy based on query complexity. % our solution Our approach leverages a multi-armed bandit algorithm, which treats each retrieval method as a distinct ``arm'' and adapts the selection process by balancing exploration and exploitation. Additionally, we introduce a dynamic reward function that balances accuracy and efficiency, penalizing methods that require more retrieval steps, even if they lead to a correct result. Our method achieves new state of the art results on multiple single-hop and multi-hop datasets while reducing retrieval costs. Our code are available at https://github.com/FUTUREEEEEE/MBA .

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