发表机构
LinkedIn Corporation（领英公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
领英为应对职位理解系统构建挑战，提出统一语义建模框架。先微调小型语言模型，再引入多适配器架构。经离线评估和在线A/B测试，该框架提升了性能、降低了复杂性，为构建行业规模文本理解系统提供实用见解。
AI 中文摘要
职位理解对领英将人才与机会相匹配的使命至关重要。该任务需将非结构化且有噪声的职位招聘信息转化为标准化或派生的职位属性，以支持众多领英产品。然而，构建一个可扩展、经济高效且高性能的职位理解系统仍具挑战。本文提出一个由小型语言模型驱动的统一语义建模框架来应对这些挑战。首先使用精心策划的合成任务对开源小型语言模型进行微调，并添加推理痕迹，这些任务共同针对分类法引导的分类和与分类法无关的实体提取。在此基础上，引入具有属性分组的多适配器架构，以促进高效的特定任务适应，同时简化跨不同下游属性的模型管理。离线评估和在线A/B测试表明，该框架在降低操作复杂性的同时显著提高了性能。我们的工作为构建行业规模的文本理解系统提供了实用见解。
英文摘要
Job understanding is critical to LinkedIn's mission of connecting talent with opportunity. This task involves transforming unstructured and noisy job postings into standardized or derived job attributes that power numerous LinkedIn products. However, building a scalable, cost-efficient, and high-performing job understanding system remains challenging. In this paper, we present a unified semantic modeling framework powered by a small language model (SLM) to address the challenges. We begin by fine-tuning an open-source SLM using a suite of carefully curated synthetic tasks augmented with reasoning traces. These tasks jointly target taxonomy-guided classification and taxonomy-agnostic entity extraction. This allows the resulting model to acquire robust zero-shot generalization for job understanding in structured and unstructured contexts. Building upon this foundation, we introduce a multi-adapter architecture with attribute grouping to facilitate efficient task-specific adaptation while streamlining model management across diverse downstream attributes. Offline evaluations and online A/B tests demonstrate significant performance improvement while reducing operational complexity. Our work provides practical insights into building industry-scale text understanding systems.
