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This paper introduces AutoScreen-FW, a framework that leverages open-source LLMs for automated resume screening, addressing data privacy concerns associated with commercial LLMs. AutoScreen-FW employs a novel method for selecting representative resume samples for in-context learning, enabling the LLM to evaluate unseen resumes based on a defined persona and criteria. Experiments demonstrate that AutoScreen-FW, using open-source LLMs, can outperform GPT-3.5-nano and, in some cases, even GPT-3.5-mini, while offering faster processing speeds.
Open-source LLMs, when carefully prompted with representative examples, can rival or even surpass smaller commercial models like GPT-3.5-nano in resume screening tasks, offering a privacy-preserving alternative.
Corporate recruiters often need to screen many resumes within a limited time, which increases their burden and may cause suitable candidates to be overlooked. To address these challenges, prior work has explored LLM-based automated resume screening. However, some methods rely on commercial LLMs, which may pose data privacy risks. Moreover, since companies typically do not make resumes with evaluation results publicly available, it remains unclear which resume samples should be used during learning to improve an LLM's judgment performance. To address these problems, we propose AutoScreen-FW, an LLM-based locally and automatically resume screening framework. AutoScreen-FW uses several methods to select a small set of representative resume samples. These samples are used for in-context learning together with a persona description and evaluation criteria, enabling open-source LLMs to act as a career advisor and evaluate unseen resumes. Experiments with multiple ground truths show that the open-source LLM judges consistently outperform GPT-5-nano. Under one ground truth setting, it also surpass GPT-5-mini. Although it is slightly weaker than GPT-5-mini under other ground-truth settings, it runs substantially faster per resume than commercial GPT models. These findings indicate the potential for deploying AutoScreen-FW locally in companies to support efficient screening while reducing recruiters'burden.
