发表机构
Technical University of Munich; Vrije Universiteit Amsterdam（慕尼黑工业大学; 阿姆斯特丹自由大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建含300份简历的平衡语料库，用LLM流水线提取软技能并检验13项假设，发现候选人多以叙事披露软技能，资历影响领导力表述率，关键词筛选会遗漏软技能。
AI 中文摘要
软技能影响构建ML赋能系统的机器学习工程师、数据科学家与软件工程师之间的协作，但目前对软技能的认知几乎全部来自需求侧：招聘广告、调查及招聘经理访谈记录了雇主的要求，而候选人自身如何表述这些能力尚未被研究；现有的简历挖掘工作要么基于关键词，无法识别叙事传达的技能，要么仅报告频率排名，未检验群体差异是否超出抽样变异。本文填补这两个缺口：使用涵盖三类岗位的300份精选简历构成的平衡语料库，通过基于大语言模型（LLM）的流水线提取明确列出及隐含叙事的软技能，该流水线已通过人工标注的基准真值验证，这是现有提取器未设计的区分；随后将需求侧文献的主张转化为关于岗位特征、资历进阶与披露风格的13项可证伪假设，通过控制家族式误差的效应量检验，使候选人侧数据能证实或反驳需求侧的说法，而非仅作例证。11项假设获支持，1项部分支持，1项被反驳：候选人通过叙事披露软技能的比例约为关键词列表的3倍，雇主最重视的领导力、协调能力与指导能力中，88%-96%通过叙事披露；资历使表述领导力的几率几乎增至原来的3倍；此前被认为普遍存在的领导力，软件工程师的表述率仅为同行的一半；技术类候选人确实表述软技能，但基于关键词的筛选会系统性遗漏这些技能。
英文摘要
Soft skills shape collaboration among ML engineers, data scientists, and software engineers building ML-enabled systems, yet what we know about them comes almost entirely from the demand side. Job advertisements, surveys, and hiring manager interviews capture what employers ask for. How candidates themselves articulate these competencies has not been studied, and existing CV-mining work is both keyword-based, so it cannot see skills conveyed through narrative, and descriptive, reporting frequency rankings without testing whether group differences exceed sampling variation. We close both gaps. Using a balanced corpus of 300 curated CVs spanning the three roles, we extract explicitly listed and implicitly narrated soft skills with an LLM-based pipeline validated against a human-annotated ground truth, a distinction that existing extractors were not designed to make. We then convert the demand-side literature's claims into 13 falsifiable hypotheses about role signatures, seniority progression, and disclosure style, and test them with effect sizes under family-wise error control, so that candidate-side data can corroborate or contradict the demand-side account rather than merely illustrate it. Eleven hypotheses are supported, one partially, and one refuted. Candidates disclose soft skills through narrative rather than keyword lists by roughly three to one, and most so for the competencies employers value most: leadership, coordination, and mentoring (88-96% narrative). Seniority nearly triples the odds of articulating leadership. That competency, assumed universal in prior work, is articulated by software engineers at half the rate of their peers. Technical candidates do articulate soft skills, but a keyword-based screening systematically misses them.
