# Extreme Multi-Label Skill Extraction Training using Large Language Models

arXiv: https://arxiv.org/abs/2307.10778 · HTML: https://mlsanity.com/paper/2307.10778/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [IDAS: Intent Discovery with Abstractive Summarization](https://mlsanity.com/paper/2305.19783/) (2023)
- [BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)](https://mlsanity.com/paper/2210.11892/) (2022)
- [Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction](https://mlsanity.com/paper/2209.05987/) (2022)
- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model](https://mlsanity.com/paper/2304.11060/) (2023)

## Built on by (most cited first, 17 of 17)

- [Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings](https://mlsanity.com/paper/2402.05617/) (2024)
- [JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching](https://mlsanity.com/paper/2402.03242/) (2024)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [Entity Linking in the Job Market Domain](https://mlsanity.com/paper/2401.17979/) (2024)
- [MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations](https://mlsanity.com/paper/2410.08319/) (2024)
- [Efficient Text Encoders for Labor Market Analysis](https://mlsanity.com/paper/2505.24640/) (2025)
- [TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/2407.05175/) (2024)
- [A Multi-Stage Framework with Taxonomy-Guided Reasoning for Occupation Classification Using Large Language Models](https://mlsanity.com/paper/2503.12989/) (2025)
- [Comparative Analysis of Encoder-Based NER and Large Language Models for Skill Extraction from Russian Job Vacancies](https://mlsanity.com/paper/2407.19816/) (2024)
- [Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker](https://mlsanity.com/paper/2511.07969/) (2025)
- [Semantic Synergy: Unlocking Policy Insights and Learning Pathways Through Advanced Skill Mapping](https://mlsanity.com/paper/2503.10094/) (2025)
- [Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomies](https://mlsanity.com/paper/2512.03195/) (2025)
- [Leveraging LLMs For Turkish Skill Extraction](https://mlsanity.com/paper/2601.22885/) (2026)
- [UniSkill: A Dataset for Matching University Curricula to Professional Competencies](https://mlsanity.com/paper/2603.03134/) (2026)
- [Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models](https://mlsanity.com/paper/2604.00006/) (2026)
- [Detecting Soft Skills in ML Engineering Roles CVs](https://mlsanity.com/paper/2608.10046/) (2026)
- [Measuring Curriculum-Labor Market Alignment at the Scale of a Program Portfolio](https://mlsanity.com/paper/2608.12356/) (2026)

## Abstract (arXiv)

Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and e-recruitment processes. Since such ads are typically formatted in free text, natural language processing (NLP) technologies are required to automatically process them. We specifically focus on the task of detecting skills (mentioned literally, or implicitly described) and linking them to a large skill ontology, making it a challenging case of extreme multi-label classification (XMLC). Given that there is no sizable labeled (training) dataset are available for this specific XMLC task, we propose techniques to leverage general Large Language Models (LLMs). We describe a cost-effective approach to generate an accurate, fully synthetic labeled dataset for skill extraction, and present a contrastive learning strategy that proves effective in the task. Our results across three skill extraction benchmarks show a consistent increase of between 15 to 25 percentage points in textitR-Precision@5 compared to previously published results that relied solely on distant supervision through literal matches.