StaffPro: an LLM Agent for Joint Staffing and Profiling
Alessio Maritan

TL;DR
StaffPro is an innovative LLM-based agent that jointly handles workforce staffing and profiling, enabling natural language optimization, continuous worker attribute estimation, and improved decision-making in personnel management.
Contribution
It introduces StaffPro, a novel LLM agent that integrates staffing and profiling with natural language interaction and continuous learning, advancing workforce management solutions.
Findings
Successfully estimates worker attributes from unstructured data
Generates high-quality, flexible schedules based on natural language objectives
Demonstrates robustness and interpretability in simulated workforce scenarios
Abstract
Large language model (LLM) agents integrate pre-trained LLMs with modular algorithmic components and have shown remarkable reasoning and decision-making abilities. In this work, we investigate their use for two tightly intertwined challenges in workforce management: staffing, i.e., the assignment and scheduling of tasks to workers, which may require team formation; and profiling, i.e., the continuous estimation of workers' skills, preferences, and other latent attributes from unstructured data. We cast these problems in a formal mathematical framework that links scheduling decisions to latent feature estimation, and we introduce StaffPro, an LLM agent that addresses staffing and profiling jointly. Differently from existing staffing solutions, StaffPro allows expressing optimization objectives using natural language, accepts textual task descriptions and provides high flexibility.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
