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arXiv · 2602.23766

UniFAR: A Unified Facet-Aware Retrieval Framework for Scientific Documents

Zheng Dou·Zhao Zhang·Deqing Wang·Yikun Ban·Fuzhen Zhuang
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Abstract

Scientific document retrieval (SDR) plays a critical role in modern scientific research, supporting knowledge discovery and evidence-based reasoning. It has evolved along two paradigms: document--document (doc-doc) retrieval driven by inter-document contrastive learning, and question--document (q-doc) retrieval emerging from LLMs and RAG for natural-language interaction. In practice, scientific workflows rely on both paradigms, requiring retrieval of related papers given a seed document and identifying relevant documents given a user question. However, existing methods typically treat these paradigms separately, hindering their complementary strengths. To address this, we propose UniFAR, a unified facet-aware retrieval framework that jointly supports doc-doc and q-doc retrieval within a shared representation space. UniFAR introduces a multi-granularity representation and aggregation module to unify the encoding of short questions and long documents, and a facet-level modeling mechanism with learnable anchors to capture structured semantic roles and complex user intents. It further adopts a facet-aware joint training strategy that integrates doc-doc and q-doc contrastive objectives with facet-level alignment, enabling unified learning from both inter-document relations and question-oriented supervision. Experiments on three benchmark datasets under both doc-doc and q-doc settings across multiple backbone models show that UniFAR consistently outperforms strong baselines and generalizes effectively across different backbones.

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Zheng Dou, Zhao Zhang, Deqing Wang, Yikun Ban, Fuzhen Zhuang. 2026-08-31. UniFAR: A Unified Facet-Aware Retrieval Framework for Scientific Documents. https://arxiv.org/abs/2602.23766

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