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This paper proposes AirRAG, a novel Retrieval-Augmented Generation (RAG) method that leverages the autonomous decision-making capabilities of large-scale language models (LLMs) to solve complex problems. AirRAG overcomes the single-solution space limitation of existing RAGs and explores diverse solutions by integrating strategic planning and efficient inference behaviors using Monte Carlo Tree Search (MCTS). We design five basic inference behaviors and extend them through MCTS to generate a tree-based inference space. We integrate self-consistency verification and inference scaling laws to explore potential inference paths and use computationally optimal strategies to allocate more inference resources to key behaviors. Experimental results demonstrate that AirRAG improves performance on complex question-answering datasets and is easily integrated with other advanced techniques and models.
Takeaways, Limitations
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Takeaways:
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Expanding the solution space for complex problems through MCTS-based strategic planning and efficient inference behavior integration.
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Exploring potential inference paths and improving performance through self-consistency verification and inference scaling laws.
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Increase efficiency through computationally optimal resource allocation strategies.
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Easy integration with other advanced technologies and models.
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Demonstrated performance improvements on complex question-answering datasets.
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Limitations:
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The computational cost of MCTS may increase. (Although not explicitly stated, the nature of MCTS suggests that this may increase computational complexity.)
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Further research is needed to determine the generalizability of the proposed five basic reasoning behaviors and their applicability to other types of problems.
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Further validation is needed regarding the limitations of the experimental dataset and generalization performance.
