从词汇基线到智能体检索增强生成：基于SFIA框架的结构化技能与责任级别提取
From Lexical Baselines to Agentic Retrieval-Augmented Generation: Structured Skill and Responsibility-Level Extraction with the SFIA Framework
- Informatics Institute of Technology（信息科技学院）
- University of Sri Jayewardenepura（斯里贾亚瓦德纳普拉大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究针对SFIA框架提出结构化技能与责任级别提取任务，比较五种策略，发现检索匹配识别技能多、生成策略更精确，显式级别决策更可靠，智能体团队无增益，提供首个可复现基线。
中文摘要 AI 辅助
自动化技能提取是劳动力规划的基础，然而大多数系统将技能表示为扁平标签，不包含技能实践所对应的责任级别这一概念。信息时代技能框架（SFIA）恰好捕捉了这一维度，定义了跨越七个责任级别的147项专业技能，但尚未有基于LLM的自动化提取方法针对SFIA的报道。我们将该任务形式化为从自由文本中结构化预测（技能，级别）对，并提出三个问题：文本能够多准确地映射到SFIA的封闭词汇表，哪些策略能够可靠地预测技能及其级别，以及智能体设计是否优于更简单的检索和提示方法？我们评估了五种策略（词汇基线、带LLM重排序的稠密检索、零样本模式约束LLM、单智能体智能体RAG，以及由检索器-匹配器-验证器组成的三智能体团队），针对专家映射的欧洲ICT角色档案，所有策略均使用我们发布的由全自动智能体管道构建的SFIA 9语料库。基于检索的匹配识别出最多的技能，而生成式策略则显著更精确；只有将级别分配作为显式决策的策略才能可靠地预测级别，基于相似性的选择不准确度超过两倍；团队使延迟加倍而未提高准确性，因此增加智能体角色并不会自动有利于封闭词汇表匹配。这些结果为针对SFIA的结构化、级别感知技能提取提供了首个可复现基线。
英文摘要
Automated skill extraction underpins workforce planning, yet most systems represent skills as flat labels with no notion of the responsibility level at which a skill is practiced. The Skills Framework for the Information Age (SFIA) captures exactly this dimension, defining 147 professional skills across seven responsibility levels, but no automated LLM-based extraction targeting SFIA has been reported. We formalize the task as structured prediction of (skill, level) pairs from free text and ask three questions: how accurately can text be mapped onto SFIA's closed vocabulary, which strategies reliably predict the level alongside the skill, and do agentic designs improve on simpler retrieval and prompting? We evaluate five strategies (a lexical baseline, dense retrieval with LLM reranking, a zero-shot schema-constrained LLM, single-agent agentic RAG, and a three-agent retriever--matcher--verifier crew) against expert-mapped European ICT role profiles, all drawing on an SFIA~9 corpus built by a fully automated agentic pipeline that we release. Retrieval-based matching identifies the most skills while generative strategies are markedly more precise; only strategies assigning the level as an explicit decision predict it reliably, with similarity-based selection more than twice as inaccurate; and the crew doubles latency without improving accuracy, so added agent roles do not automatically benefit closed-taxonomy matching. These results provide the first reproducible baseline for structured, level-aware skill extraction against SFIA.
