Presentation Information
[1F4-OS-10b-05]AutoScreen-FW: A Few-Shot In-Context Learning Framework for Resume Screening with LLMs
〇Zhelin Xu1, Shuhei Yamamoto1 (1. University of Tsukuba)
Keywords:
LLM-as-a-Judge,In-Context Learning,Resume Screening
In job-hunting activities, students are expected to submit high-quality resumes to pass document screening. However, university career support centers often cannot provide sufficient feedback to all students due to limited staff and time. Likewise, in companies, screening a large number of resumes places a considerable burden on hiring personnel. To address these challenges, recent studies have proposed LLM-based methods for automated resume screening. However, these methods are not directly applicable to Japan's unique job-hunting system. Therefore, this study develops an LLM-based automated resume screening framework named AutoScreen-FW, tailored to Japanese new graduate recruitment. Specifically, we employ multiple sample selection strategies to extract a small number of representative resume samples. These samples are then used to perform in-context learning, enabling the LLM to assess unseen resume automatically. Experimental results show that the proposed framework enables an open-source LLM to achieve resume evaluation performance comparable to, or better than, GPT-series models.