# Leveraging LLMs For Turkish Skill Extraction

arXiv: https://arxiv.org/abs/2601.22885 · HTML: https://mlsanity.com/paper/2601.22885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 47 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 23 of 23)

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- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations](https://mlsanity.com/paper/2308.14186/) (2023)
- [EuroBERT: Scaling Multilingual Encoders for European Languages](https://mlsanity.com/paper/2503.05500/) (2025)
- [Learning Representations for Soft Skill Matching](https://mlsanity.com/paper/1807.07741/) (2018)
- [Extreme Multi-Label Skill Extraction Training using Large Language Models](https://mlsanity.com/paper/2307.10778/) (2023)
- [Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction](https://mlsanity.com/paper/2209.05987/) (2022)
- [Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers](https://mlsanity.com/paper/2307.03539/) (2023)
- [Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings](https://mlsanity.com/paper/2402.05617/) (2024)
- [Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning](https://mlsanity.com/paper/2205.01381/) (2022)
- [JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching](https://mlsanity.com/paper/2402.03242/) (2024)
- [SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model](https://mlsanity.com/paper/2304.11060/) (2023)
- [Rethinking Skill Extraction in the Job Market Domain using Large Language Models](https://mlsanity.com/paper/2402.03832/) (2024)
- [Entity Linking in the Job Market Domain](https://mlsanity.com/paper/2401.17979/) (2024)
- [LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models](https://mlsanity.com/paper/2309.09708/) (2023)
- [NNOSE: Nearest Neighbor Occupational Skill Extraction](https://mlsanity.com/paper/2401.17092/) (2024)
- [CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations](https://mlsanity.com/paper/2503.02056/) (2025)
- [Efficient Text Encoders for Labor Market Analysis](https://mlsanity.com/paper/2505.24640/) (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)

## Abstract (arXiv)

Skill extraction is a critical component of modern recruitment systems, enabling efficient job matching, personalized recommendations, and labor market analysis. Despite T"urkiye's significant role in the global workforce, Turkish, a morphologically complex language, lacks both a skill taxonomy and a dedicated skill extraction dataset, resulting in underexplored research in skill extraction for Turkish. This article seeks the answers to three research questions: 1) How can skill extraction be effectively performed for this language, in light of its low resource nature? 2)~What is the most promising model? 3) What is the impact of different Large Language Models (LLMs) and prompting strategies on skill extraction (i.e., dynamic vs. static few-shot samples, varying context information, and encouraging causal reasoning)? The article introduces the first Turkish skill extraction dataset and performance evaluations of automated skill extraction using LLMs. The manually annotated dataset contains 4,819 labeled skill spans from 327 job postings across different occupation areas. The use of LLM outperforms supervised sequence labeling when used in an end-to-end pipeline, aligning extracted spans with standardized skills in the ESCO taxonomy more effectively. The best-performing configuration, utilizing Claude Sonnet 3.7 with dynamic few-shot prompting for skill identification, embedding-based retrieval, and LLM-based reranking for skill linking, achieves an end-to-end performance of 0.56, positioning Turkish alongside similar studies in other languages, which are few in the literature. Our findings suggest that LLMs can improve skill extraction performance in low-resource settings, and we hope that our work will accelerate similar research on skill extraction for underrepresented languages.