ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement
<|reference_start|>ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement: 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.<|reference_end|>
@article{zinjad2024resumeflow:,
    title={ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume
  Generation and Refinement},
    author={Saurabh Bhausaheb Zinjad, Amrita Bhattacharjee, Amey Bhilegaonkar,
  Huan Liu},
    journal={arXiv preprint arXiv:2402.06221},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06221},
    primaryClass={cs.CL cs.IR}
}