演示混合用于文本图理解与问答
Mixture of Demonstrations for Textual Graph Understanding and Question Answering
AI总结：
本文提出MixDemo框架，通过MoE机制选择信息丰富的演示，结合查询特定图编码器减少噪声，提升文本图问答性能。
摘要：
基于文本图的检索增强生成（GraphRAG）已成为增强大型语言模型（LLMs）在领域特定问答中的强大范式。尽管现有方法主要关注零样本GraphRAG，选择高质量演示对于提升推理和回答准确性至关重要。此外，近期研究表明检索到的子图往往包含无关信息，这会降低推理性能。在本文中，我们提出MixDemo，一种增强的GraphRAG框架，通过混合专家（MoE）机制在多样的问题上下文中选择最有信息量的演示。为进一步减少检索到的子图中的噪声，我们引入了查询特定的图编码器，该编码器会选择性地关注与查询最相关的信息。在多个文本图基准上的广泛实验表明，MixDemo显著优于现有方法。
英文摘要：
Textual graph-based retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) in domain-specific question answering. While existing approaches primarily focus on zero-shot GraphRAG, selecting high-quality demonstrations is crucial for improving reasoning and answer accuracy. Furthermore, recent studies have shown that retrieved subgraphs often contain irrelevant information, which can degrade reasoning performance. In this paper, we propose MixDemo, a novel GraphRAG framework enhanced with a Mixture-of-Experts (MoE) mechanism for selecting the most informative demonstrations under diverse question contexts. To further reduce noise in the retrieved subgraphs, we introduce a query-specific graph encoder that selectively attends to information most relevant to the query. Extensive experiments across multiple textual graph benchmarks show that MixDemo significantly outperforms existing methods.
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。
