Mixture of Demonstrations for Textual Graph Understanding and Question Answering
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 no...
| Authors | Yukun Wu, Lihui Liu |
|---|---|
| Primary category | cs.IR |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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