通过专家混合机制精选最优示例，提升文本图问答的准确率。
Mixture of Demonstrations for Textual Graph Understanding and Question Answering
- 用专家混合模型动态选择最相关示例
- 在多个基准上超越现有方法，显著提升回答准确率
- 适合需要精准推理的领域问答任务
基于文本图的检索增强生成（GraphRAG）已成为提升大语言模型在特定领域问答性能的强大范式。现有方法多聚焦于零样本GraphRAG，而高质量示例的选择对推理与答案准确性至关重要。此外，近期研究发现，检索到的子图常包含无关信息，会降低推理表现。本文提出MixDemo，一种结合专家混合（MoE）机制的新型GraphRAG框架，可在不同问题上下文中筛选最具信息量的示例。为进一步减少检索子图中的噪声，我们引入查询感知图编码器，仅关注与查询最相关的信息。在多个文本图基准上的大量实验表明，MixDemo显著优于现有方法。
原文摘要 · Abstract (English)
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.
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