# LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models

arXiv: https://arxiv.org/abs/2309.09708 · HTML: https://mlsanity.com/paper/2309.09708/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://mlsanity.com/paper/2204.07931/) (2022)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning](https://mlsanity.com/paper/2205.01381/) (2022)
- [Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions](https://mlsanity.com/paper/2110.00078/) (2021)

## Built on by (most cited first, 6 of 6)

- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [JobHop: A Large-Scale Dataset of Career Trajectories](https://mlsanity.com/paper/2505.07653/) (2025)
- [A Multi-Stage Framework with Taxonomy-Guided Reasoning for Occupation Classification Using Large Language Models](https://mlsanity.com/paper/2503.12989/) (2025)
- [JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes](https://mlsanity.com/paper/2607.11715/) (2026)
- [Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification](https://mlsanity.com/paper/2412.12744/) (2024)
- [Leveraging LLMs For Turkish Skill Extraction](https://mlsanity.com/paper/2601.22885/) (2026)

## Abstract (arXiv)

Automated occupation extraction and standardization from free-text job postings and resumes are crucial for applications like job recommendation and labor market policy formation. This paper introduces LLM4Jobs, a novel unsupervised methodology that taps into the capabilities of large language models (LLMs) for occupation coding. LLM4Jobs uniquely harnesses both the natural language understanding and generation capacities of LLMs. Evaluated on rigorous experimentation on synthetic and real-world datasets, we demonstrate that LLM4Jobs consistently surpasses unsupervised state-of-the-art benchmarks, demonstrating its versatility across diverse datasets and granularities. As a side result of our work, we present both synthetic and real-world datasets, which may be instrumental for subsequent research in this domain. Overall, this investigation highlights the promise of contemporary LLMs for the intricate task of occupation extraction and standardization, laying the foundation for a robust and adaptable framework relevant to both research and industrial contexts.