ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement

TL;DR

ResumeFlow tackles the laborious task of tailoring resumes to specific job postings by presenting an end-to-end LLM-facilitated pipeline. The system decomposes the process into three modules—User Data Extractor, Job Details Extractor, and Resume Generator—to produce a job-aligned resume in seconds without fine-tuning, with outputs in JSON and LaTeX-formatted PDF. It introduces novel evaluation metrics for alignment and content preservation in both token and latent spaces, addressing hallucination concerns and providing user-facing transparency. The work demonstrates practical feasibility with GPT-4 and Gemini backends and points to future expansion toward additional LLMs and retrieval-augmented techniques to further improve reliability and coverage.

Abstract

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.

Figures (3)

• Figure 1: Our overall ResumeFlow pipeline for LLM-facilitated generation of job-aligned resumes.
• Figure 2: Our easy-to-use User Interface.
• Figure 3: Truncated screenshot of ResumeFlow generating a job-aligned resume.