arXiv:2602.07739v2 Announce Type: replace-cross Abstract: Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space. However, natural language exhibits hierarchical structure from broad topics to specific entities that Euclidean embeddings fail to preserve, causing semantically distant documents to appear spuriously similar and increasing hallucination risk. To address these limitations, we introduce hyperbolic dense retrieval, developing two model variants in the Lorentz model of h
The paper addresses the inherent limitations of Euclidean embeddings in current RAG systems, a critical component of evolving AI applications, as the demand for more accurate and less 'hallucinatory' AI outputs increases.
Improving dense retrieval mechanisms in RAG directly impacts the reliability and efficacy of AI models across various applications, reducing hallucination risk and enhancing semantic understanding for more robust decision-making.
The shift to hyperbolic embeddings in dense retrieval fundamentally changes how RAG systems process and interpret natural language, offering a more nuanced representation of hierarchical data than traditional Euclidean methods.
- · AI researchers
- · NLP developers
- · Generative AI companies
- · Data scientists
- · Companies reliant on less sophisticated RAG
- · Firms experiencing high AI hallucination rates
Retrieval-augmented generation systems will become significantly more accurate and reliable by better capturing the hierarchical nature of language.
The improved reliability of RAG could lead to broader and more confident adoption of AI assistants and knowledge systems in critical domains like finance and healthcare.
More sophisticated and less error-prone AI systems could accelerate the development and deployment of autonomous AI agents, blurring the lines between human and machine capabilities in complex workflows.
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Read at arXiv cs.AI