Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning

2025-01-13

AI Summary

This study addresses the challenge of extracting critical employment information—such as work modality, compensation structure, educational/experiential requirements, and non-monetary benefits—from job postings, where such attributes are often implicitly expressed and easily overlooked. We propose an end-to-end fine-grained parsing framework that uniquely integrates semantic chunking, retrieval-augmented generation (RAG), and fine-tuned DistilBERT to jointly model contextual semantics and domain-specific knowledge. This synergy significantly improves recall and precision for hard-to-detect features, including implicit remote-work indicators and non-salary compensations. Evaluated on 1.2 million real-world job advertisements, our method achieves a 27% average F1-score gain on key variables and reduces mislabeling rates by 41%. The framework delivers scalable, robust, and high-confidence structured data to support rigorous labor market analysis.

Technology Category

• Natural Language Processing: Syntax — Tagging, Chunking & Parsing
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Job Information Extraction
• Recruitment Advertisements
• Labor Market Analysis

Innovation

• LLM Fine-tuning
• Job Ad Analysis
• Market Insights Enhancement