MIXRAG : 基于专家混合的检索增强生成用于文本图理解与问答
MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
AI总结：
MIXRAG通过专家混合图-RAG框架，利用多个专门化图检索器和动态路由控制器，提升复杂查询处理和噪声过滤能力，实现文本图理解和问答任务的高性能表现。
摘要：
大型语言模型（LLMs）在广泛的应用中取得了令人印象深刻的性能。然而，由于其依赖静态预训练语料库，它们在知识密集型领域常常因幻觉而出现问题。为了解决这一限制，检索增强生成（RAG）通过在推理过程中整合外部知识源来增强LLMs。其中，文本图提供了结构化且语义丰富的信息，支持更精确和可解释的推理。这导致了基于图的RAG系统日益受到关注。尽管具有潜力，大多数现有方法依赖于单一检索器来识别相关子图，这限制了它们捕捉复杂查询多样方面的能力。此外，这些系统往往难以准确判断检索内容的相关性，容易受到无关噪声的干扰。为了解决这些挑战，本文提出了MIXRAG，一种专家混合图-RAG框架，引入了多个专门的图检索器和动态路由控制器，以更好地处理多样化的查询意图。每个检索器都训练以专注于图语义的特定方面，如实体、关系或子图拓扑。一个专家混合模块根据输入查询自适应地选择和融合相关检索器。为了减少检索信息中的噪声，我们引入了一个查询感知的图编码器，仔细分析检索子图中的关系，突出最相关部分，同时降低不必要噪声的权重。实验证果表明，我们的方法实现了最先进的性能，并且在各种基线中始终占据优势。MIXRAG在不同领域的图基任务中均表现出色。代码将在论文接受后发布。
英文摘要：
Large Language Models (LLMs) have achieved impressive performance across a wide range of applications. However, they often suffer from hallucinations in knowledge-intensive domains due to their reliance on static pretraining corpora. To address this limitation, Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating external knowledge sources during inference. Among these sources, textual graphs provide structured and semantically rich information that supports more precise and interpretable reasoning. This has led to growing interest in graph-based RAG systems. Despite their potential, most existing approaches rely on a single retriever to identify relevant subgraphs, which limits their ability to capture the diverse aspects of complex queries. Moreover, these systems often struggle to accurately judge the relevance of retrieved content, making them prone to distraction by irrelevant noise. To address these challenges, in this paper, we propose MIXRAG, a Mixture-of-Experts Graph-RAG framework that introduces multiple specialized graph retrievers and a dynamic routing controller to better handle diverse query intents. Each retriever is trained to focus on a specific aspect of graph semantics, such as entities, relations, or subgraph topology. A Mixture-of-Experts module adaptively selects and fuses relevant retrievers based on the input query. To reduce noise in the retrieved information, we introduce a query-aware GraphEncoder that carefully analyzes relationships within the retrieved subgraphs, highlighting the most relevant parts while down-weighting unnecessary noise. Empirical results demonstrate that our method achieves state-of-the-art performance and consistently outperforms various baselines. MIXRAG is effective across a wide range of graph-based tasks in different domains. The code will be released upon paper acceptance.
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。
