Competence-Preserving Resume Perturbations Expose Presentation Sensitivity in LLM Screening

2026-09-14

AI Summary

研究通过构建控制下的简历变异来测试大型语言模型在筛选简历时对表面形式变化的敏感度，发现即使在保持能力不变的情况下，不同呈现方式会导致决策不稳定。

Abstract

Resume screeners must infer job-relevant competence from resumes whose presentation can vary substantially in wording, structure, stylistic polish, and document extraction quality. Ideally, such surface variation should not change decisions when the underlying qualification evidence is unchanged. We introduce a controlled audit of this property, constructing occupation-grounded candidate profiles at controlled competence levels and rendering each profile into multiple resume presentations. A deterministic validation gate excludes variants that alter the underlying evidence before scoring. Across six open instruction-tuned LLM conditions, we find a clear disconnect between screening validity and presentation stability. Llama-3.1-8B with its native chat template achieves the strongest validity ($0.781$) yet reverses $29.6\%$ of matched pairwise decisions under competence-preserving presentation changes; Mistral-7B-v0.3 reaches validity $0.644$ with a $41.4\%$ flip rate. Native chat formatting improves validity for several chat-tuned models but does not remove this instability. These results show that resume-screening evaluations should assess not only whether a system identifies stronger candidates, but also whether those decisions remain stable when the same competence evidence is presented differently.

Problem

• resume screening
• competence inference
• presentation variation
• decision stability

Innovation

• Competence-Preserving
• Resume Presentation Sensitivity
• LLM Screening
• Controlled Audit