RAGShaper: 通过自动化数据合成激发复杂的代理RAG技能
RAGShaper: Eliciting Sophisticated Agentic RAG Skills via Automated Data Synthesis
AI总结：
RAGShaper通过自动化数据合成构建高质量RAG任务和代理轨迹，提升模型在复杂检索任务中的鲁棒性。
摘要：
代理式检索增强生成（RAG）使大型语言模型能够自主规划和检索信息以解决复杂问题。然而，开发稳健的代理受到高质量训练数据的匮乏所阻碍，这些数据无法反映现实世界检索环境中的噪声和复杂性。传统手动标注不可扩展且往往无法捕捉处理检索失败所需的动态推理策略。为弥补这一差距，我们引入了RAGShaper，一种新的数据合成框架，旨在自动化构建RAG任务和稳健代理轨迹。RAGShaper集成了一个InfoCurator，用于构建密集的信息树，其中包含覆盖感知和认知层次的对抗性干扰项。此外，我们提出了一种受限导航策略，迫使教师代理面对这些干扰项，从而产生明确展示错误修正和噪声拒绝的轨迹。全面实验证实，训练在我们合成语料库上的模型在噪声密集和复杂检索任务中显著优于现有基线。
英文摘要：
Agentic Retrieval-Augmented Generation (RAG) empowers large language models to autonomously plan and retrieve information for complex problem-solving. However, the development of robust agents is hindered by the scarcity of high-quality training data that reflects the noise and complexity of real-world retrieval environments. Conventional manual annotation is unscalable and often fails to capture the dynamic reasoning strategies required to handle retrieval failures. To bridge this gap, we introduce RAGShaper, a novel data synthesis framework designed to automate the construction of RAG tasks and robust agent trajectories. RAGShaper incorporates an InfoCurator to build dense information trees enriched with adversarial distractors spanning Perception and Cognition levels. Furthermore, we propose a constrained navigation strategy that forces a teacher agent to confront these distractors, thereby eliciting trajectories that explicitly demonstrate error correction and noise rejection. Comprehensive experiments confirm that models trained on our synthesized corpus significantly outperform existing baselines, exhibiting superior robustness in noise-intensive and complex retrieval tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
