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arXiv AI By Jinliang Liu, Jiale Bai, Shaoning Zeng

Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation

Read the original on arXiv AI →

The paper introduces ParallaxRAG, a multi-view retrieval-augmented generation framework that addresses multi-hop reasoning challenges in large language models over knowledge graphs. It identifies that transformer attention heads specialize in distinct semantic relations across reasoning stages, forming a hop-aligned relay pattern, and proposes decoupling queries and KGs into head-specific semantic spaces to enforce relational diversity. Experiments on WebQSP, CWQ, and BioASQ show state‑of‑the‑art performance, reduced hallucination, and strong generalization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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