DUPLet: Dynamic User Modeling for Personalized Cover Letter Generation

Composing tailored cover letters for specific job applications is challenging, particularly when applying for multiple jobs. We present DUPLet, an interactive system that leverages large language models (LLMs) to produce more personalized and authentic cover letters. The key novelty of our system lies in dynamic user modeling that leverages a natural-language description of the user's profile and preferences, which adapts over time based on provided input and interactions with the system. Based on a resume, job description, and a series of LLM-generated, job-specific questions, DUPLet creates an initial cover letter draft that users can then iteratively refine. We conducted a preliminary evaluation of DUPLet by simulating user interactions with LLMs and using the LLM-as-a-judge paradigm to rate the cover letters on dimensions, such as personalization and authenticity. Results indicate that cover letter variants that involve the user and leverage a dynamically refined natural-language user profile tend to receive higher ratings.