Crafting the ideal, job-specific resume is a challenging task for many job applicants, especially for early-career applicants. While it is highly recommended that applicants tailor their resume to the specific role they are applying for, manually tailoring resumes to job descriptions and role-specific requirements is often (1) extremely time-consuming, and (2) prone to human errors. Furthermore, performing such a tailoring step at scale while applying to several roles may result in a lack of quality of the edited resumes. To tackle this problem, in this demo paper, we propose ResumeFlow: a Large Language Model (LLM) aided tool that enables an end user to simply provide their detailed resume and the desired job posting, and obtain a personalized resume specifically tailored to that specific job posting in the matter of a few seconds. Our proposed pipeline leverages the language understanding and information extraction capabilities of state-of-the-art LLMs such as OpenAI's GPT-4 and Google's Gemini, in order to (1) extract details from a job description, (2) extract role-specific details from the user-provided resume, and then (3) use these to refine and generate a role-specific resume for the user. Our easy-to-use tool leverages the user-chosen LLM in a completely off-the-shelf manner, thus requiring no fine-tuning. We demonstrate the effectiveness of our tool via a video demo and propose novel task-specific evaluation metrics to control for alignment and hallucination. Our tool is available at https://job-aligned-resume.streamlit.app.

翻译：为特定岗位量身定制理想简历对许多求职者（尤其是早期职业者）而言颇具挑战。尽管专家强烈建议求职者根据应聘岗位定制简历，但手动匹配职位描述和岗位特定要求往往存在两大问题：（1）极其耗时；（2）易出现人为错误。更甚者，当同时申请多个职位时，大规模进行此类定制操作可能导致修改后的简历质量参差不齐。为解决该问题，本文提出ResumeFlow：一种基于大语言模型（LLM）的辅助工具，终端用户仅需提供详细简历及目标职位描述，即可在数秒内获得针对该职位的个性化简历。该流水线利用OpenAI GPT-4、Google Gemini等前沿LLM的语言理解与信息抽取能力，实现三步操作：（1）提取职位描述关键信息；（2）从用户简历中提取岗位相关细节；（3）基于上述信息优化并生成岗位定制简历。本工具采用即用型（off-the-shelf）策略直接调用用户选择的LLM，无需微调。通过视频演示验证工具有效性，并提出了用于控制对齐性与幻觉现象的新型任务特定评估指标。工具访问地址：https://job-aligned-resume.streamlit.app