# Rethinking Skill Extraction in the Job Market Domain using Large Language Models

arXiv: https://arxiv.org/abs/2402.03832 · HTML: https://mlsanity.com/paper/2402.03832/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 14 of 14)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://mlsanity.com/paper/2203.08410/) (2022)
- [An Empirical Study on Information Extraction using Large Language Models](https://mlsanity.com/paper/2305.14450/) (2023)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [Learning Representations for Soft Skill Matching](https://mlsanity.com/paper/1807.07741/) (2018)
- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [Skill Extraction from Job Postings using Weak Supervision](https://mlsanity.com/paper/2209.08071/) (2022)
- [Salience and Market-aware Skill Extraction for Job Targeting](https://mlsanity.com/paper/2005.13094/) (2020)
- ["FIJO": a French Insurance Soft Skill Detection Dataset](https://mlsanity.com/paper/2204.05208/) (2022)
- [SkillNER: Mining and Mapping Soft Skills from any Text](https://mlsanity.com/paper/2101.11431/) (2021)

## Built on by (most cited first, 11 of 11)

- [Gender Bias in LLM-generated Interview Responses](https://mlsanity.com/paper/2410.20739/) (2024)
- [llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models](https://mlsanity.com/paper/2406.04528/) (2024)
- [Prompt Engineer: Analyzing Hard and Soft Skill Requirements in the AI Job Market](https://mlsanity.com/paper/2506.00058/) (2025)
- [Noise-Aware Named Entity Recognition for Historical VET Documents](https://mlsanity.com/paper/2601.00488/) (2026)
- [Tec-Habilidad: Skill Classification for Bridging Education and Employment](https://mlsanity.com/paper/2503.03932/) (2025)
- [NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching](https://mlsanity.com/paper/2506.19058/) (2025)
- [Leveraging LLMs For Turkish Skill Extraction](https://mlsanity.com/paper/2601.22885/) (2026)
- [Job Skill Extraction via LLM-Centric Multi-Module Framework](https://mlsanity.com/paper/2604.21525/) (2026)
- [Forecasting Green Skill Demand in the Automotive Industry: Evidence from Online Job Postings](https://mlsanity.com/paper/2605.05280/) (2026)
- [Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2606.31692/) (2026)
- [From Lexical Baselines to Agentic Retrieval-Augmented Generation: Structured Skill and Responsibility-Level Extraction with the SFIA Framework](https://mlsanity.com/paper/2609.35806/) (2026)

## Abstract (arXiv)

Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes. The task is commonly tackled by training supervised models using a sequence labeling approach with BIO tags. However, the reliance on manually annotated data limits the generalizability of such approaches. Moreover, the common BIO setting limits the ability of the models to capture complex skill patterns and handle ambiguous mentions. In this paper, we explore the use of in-context learning to overcome these challenges, on a benchmark of 6 uniformized skill extraction datasets. Our approach leverages the few-shot learning capabilities of large language models (LLMs) to identify and extract skills from sentences. We show that LLMs, despite not being on par with traditional supervised models in terms of performance, can better handle syntactically complex skill mentions in skill extraction tasks.