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arXiv · 2605.27441

A Unified Structured Query Understanding Framework for Industrial Semantic Search

Ping Liu·Qianqi Shen·Jianqiang Shen·Chunnan Yao·Kevin Kao·Rajat Arora·Dan Xu·Baofen Zheng·Yunxiang Ren·Benjamin Le·Ali Hooshmand·Igor Lapchuk·Juan Bottaro·Raghavan Muthuregunathan·Caleb Johnson·Liangjie Hong·Jingwei Wu·Wenjing Zhang
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Abstract

Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components. While individually optimizable, this fragmented architecture incurs high maintenance overhead and results in inconsistent behaviors, particularly for long-tail queries. In this work, we propose and deploy a unified structured query understanding system that consolidates these heterogeneous functions into a single Small Language Model (SLM) that performs schema-constrained generation. To address the data bottlenecks inherent in unified modeling, we introduce Query Illuminator, a dual-purpose framework serving as: (i) a teacher model for high-quality auto-annotation and distillation, and (ii) a surrogate judge for scalable evaluation where human labels are scarce. We validate this approach through extensive offline and online tests within LinkedIn's Job Search system. Furthermore, we demonstrate the framework's horizontal extensibility through a cross-domain case study on People Search. The results show improved user engagement and reduced operational costs, achieved while satisfying strict low-latency serving constraints on limited GPU resources.

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Ping Liu, Qianqi Shen, Jianqiang Shen, Chunnan Yao, Kevin Kao, Rajat Arora, Dan Xu, Baofen Zheng, Yunxiang Ren, Benjamin Le, Ali Hooshmand, Igor Lapchuk, Juan Bottaro, Raghavan Muthuregunathan, Caleb Johnson, Liangjie Hong, Jingwei Wu, Wenjing Zhang. 2026-05-22. A Unified Structured Query Understanding Framework for Industrial Semantic Search. https://doi.org/10.1145/3770855.3818312

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