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Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction

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arxiv 2410.12052 v1 pith:4A44J5FZ submitted 2024-10-15 cs.CL

Amirhossein Herandi , Yitao Li , Zhanlin Liu , Ximin Hu , Xiao Cai This is my paper · ORCID
classification cs.CL
keywords extractionskillskill-llmapproachlightllmsmodelsota
verification ladder T0 review T1 audit T2 compute T3 formal
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Accurate skill extraction from job descriptions is crucial in the hiring process but remains challenging. Named Entity Recognition (NER) is a common approach used to address this issue. With the demonstrated success of large language models (LLMs) in various NLP tasks, including NER, we propose fine-tuning a specialized Skill-LLM and a light weight model to improve the precision and quality of skill extraction. In our study, we evaluated the fine-tuned Skill-LLM and the light weight model using a benchmark dataset and compared its performance against state-of-the-art (SOTA) methods. Our results show that this approach outperforms existing SOTA techniques.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Well Do LLMs Predict Prerequisite Skills? Zero-Shot Comparison to Expert-Defined Concepts

    cs.IR 2025-07 reject novelty 5.0 of 10

    Zero-shot LLM prompts recover ESCO prerequisite lists with BERTScore F1 around 0.82, but the evaluation lacks baselines and contamination checks, so the result does not establish true inference.

  2. Reading Between the Lines: Classifying Resume Seniority with Large Language Models

    cs.CL 2025-09 conditional novelty 4.0 of 10

    Fine-tuned RoBERTa reached 90.6% accuracy on resume seniority classification using a new hybrid dataset, outperforming zero-shot GPT-4 and a TF-IDF baseline, though evaluation details are incomplete.

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