# Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning

arXiv: https://arxiv.org/abs/2501.07663 · HTML: https://mlsanity.com/paper/2501.07663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2025.

## Abstract (arXiv)

This paper explores the application of large language models (LLMs) to extract nuanced and complex job features from unstructured job postings. Using a dataset of 1.2 million job postings provided by AdeptID, we developed a robust pipeline to identify and classify variables such as remote work availability, remuneration structures, educational requirements, and work experience preferences. Our methodology combines semantic chunking, retrieval-augmented generation (RAG), and fine-tuning DistilBERT models to overcome the limitations of traditional parsing tools. By leveraging these techniques, we achieved significant improvements in identifying variables often mislabeled or overlooked, such as non-salary-based compensation and inferred remote work categories. We present a comprehensive evaluation of our fine-tuned models and analyze their strengths, limitations, and potential for scaling. This work highlights the promise of LLMs in labor market analytics, providing a foundation for more accurate and actionable insights into job data.