Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning

• URL: http://arxiv.org/abs/2501.07663v1
• Date: Mon, 13 Jan 2025 19:49:49 GMT
• Title: Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning
• Authors: Karishma Thakrar, Nick Young,
• Abstract summary: We develop a robust pipeline to identify 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. We present a comprehensive evaluation of our fine-tuned models and analyze their strengths, limitations, and potential for scaling.
• Score: 0.0
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• Abstract: 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.

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