ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume
  Generation and Refinement
Saurabh Bhausaheb Zinjad Amrita Bhattacharjee Amey Bhilegaonkar Huan Liu
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.