Structured Knowledge Representation through Contextual Pages for Retrieval-Augmented Generation
Xinze Li, Zhenghao Liu, Haidong Xin, Yukun Yan, Shuo Wang, Zheni Zeng, Sen Mei, Ge Yu, Maosong Sun

TL;DR
PAGER introduces a page-driven framework that organizes external knowledge into structured pages for retrieval-augmented generation, improving the coherence and quality of knowledge representations in LLMs.
Contribution
It proposes a novel, structured, page-based knowledge representation method that enhances retrieval and integration of external knowledge in RAG models.
Findings
Outperforms all RAG baselines on multiple benchmarks.
Constructs higher-quality, information-dense knowledge representations.
Better mitigates knowledge conflicts and improves external knowledge utilization.
Abstract
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge. Recently, some works have incorporated iterative knowledge accumulation processes into RAG models to progressively accumulate and refine query-related knowledge, thereby constructing more comprehensive knowledge representations. However, these iterative processes often lack a coherent organizational structure, which limits the construction of more comprehensive and cohesive knowledge representations. To address this, we propose PAGER, a page-driven autonomous knowledge representation framework for RAG. PAGER first prompts an LLM to construct a structured cognitive outline for a given question, which consists of multiple slots representing a distinct knowledge aspect. Then, PAGER iteratively retrieves and refines relevant documents to populate each slot, ultimately constructing…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Information Retrieval and Search Behavior
