Alibaba: AirRAG Enhances Complex QA Reasoning Capabilities!

🌟 Introduction 1️⃣ As the complexity of tasks increases, RAG faces new challenges, including the difficulty of retrieving sufficient knowledge in a single query and understanding the complex reasoning logic in questions. 2️⃣ This article proposes AirRAG, which activates intrinsic reasoning capabilities and expands the solution space by introducing Monte Carlo Tree Search (MCTS) and a self-consistency mechanism. 🌟 Method 1️⃣ AirRAG designs five basic reasoning actions: system analysis, direct answering, retrieval answering, query transformation, and summarization answering. These actions can address various problems in different scenarios, including complex issues that require step-by-step or parallel querying. 2️⃣ By introducing MCTS and a self-consistency mechanism, AirRAG achieves controllable reasoning path generation and efficient reasoning expansion. 3️⃣ To accurately select answers from multiple reasoning paths, AirRAG combines a voting method and a process supervision reward model. 4️⃣ AirRAG has a flexible architecture that can easily integrate other advanced methods as action branches. 🌟 Effect 1️⃣ As the reasoning computational load increases, AirRAG can achieve significant performance improvements. 2️⃣ In multiple complex task tests, AirRAG shows obvious advantages in both accuracy and efficiency compared to traditional RAG methods. 3️⃣ Specific data indicates that AirRAG outperforms existing methods in zero-shot and few-shot tasks, especially in scenarios requiring multi-step reasoning.