Multi-Facet Blending for Faceted Query-by-Example Retrieval

• URL: http://arxiv.org/abs/2412.01443v1
• Date: Mon, 02 Dec 2024 12:32:19 GMT
• Title: Multi-Facet Blending for Faceted Query-by-Example Retrieval
• Authors: Heejin Do, Sangwon Ryu, Jonghwi Kim, Gary Geunbae Lee,
• Abstract summary: We propose a multi-facet blending (FaBle) augmentation method, which exploits modularity by decomposing and recomposing to explicitly synthesize facet-specific training sets. Our modularization eliminates the need for pre-defined facet knowledge or labels. FaBle augmentation on 1K documents remarkably assists training in obtaining facet conditional embeddings.
• Score: 5.156059061769101
• License:
• Abstract: With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attention. However, prior approaches mainly depend on document-level comparisons using basic indicators like citations due to the lack of facet-level relevance datasets; yet, this limits their use to citation-based domains and fails to capture the intricacies of facet constraints. In this paper, we propose a multi-facet blending (FaBle) augmentation method, which exploits modularity by decomposing and recomposing to explicitly synthesize facet-specific training sets. We automatically decompose documents into facet units and generate (ir)relevant pairs by leveraging LLMs' intrinsic distinguishing capabilities; then, dynamically recomposing the units leads to facet-wise relevance-informed document pairs. Our modularization eliminates the need for pre-defined facet knowledge or labels. Further, to prove the FaBle's efficacy in a new domain beyond citation-based scientific paper retrieval, we release a benchmark dataset for educational exam item QBE. FaBle augmentation on 1K documents remarkably assists training in obtaining facet conditional embeddings.

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