Scholarlink

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval. However, existing multi-hop RAG benchmarks are limited by shallow reasoning depth, simple retrieval structures, and non-descriptive answers. We introduce EcoRAG, a novel multi-hop economic QA benchmark built on knowledge graphs (KGs). EcoRAG extends retrieval depth to seven-hop reasoning, incorporates complex subgraph structures, and leverages domain-specific economic knowledge. It enables a more realistic evaluation of retrieval and reasoning by bridging the gap between structured knowledge sources and generative models, while also providing a reusable benchmark to advance multi-hop RAG research.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

EcoRAG: A Multi-hop Economic QA Benchmark for Retrieval Augmented Generation Using Knowledge Graphs

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Hanieh Khorashadizadeh,
  • Sanju Tiwari,
  • Farah Benamara,
  • Nandana Mihindukulasooriya,
  • Jinghua Groppe,
  • Soror Sahri,
  • Morteza Ezzabady,
  • Frédéric Ieng,
  • Sven Groppe

摘要

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval. However, existing multi-hop RAG benchmarks are limited by shallow reasoning depth, simple retrieval structures, and non-descriptive answers. We introduce EcoRAG, a novel multi-hop economic QA benchmark built on knowledge graphs (KGs). EcoRAG extends retrieval depth to seven-hop reasoning, incorporates complex subgraph structures, and leverages domain-specific economic knowledge. It enables a more realistic evaluation of retrieval and reasoning by bridging the gap between structured knowledge sources and generative models, while also providing a reusable benchmark to advance multi-hop RAG research.