# SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model

arXiv: https://arxiv.org/abs/2304.11060 · HTML: https://mlsanity.com/paper/2304.11060/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 17 Apr 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)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://mlsanity.com/paper/2204.07931/) (2022)
- [Learning Representations for Soft Skill Matching](https://mlsanity.com/paper/1807.07741/) (2018)
- [DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matching](https://mlsanity.com/paper/2104.01966/) (2021)
- [Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning](https://mlsanity.com/paper/2205.01381/) (2022)
- [Skill Extraction from Job Postings using Weak Supervision](https://mlsanity.com/paper/2209.08071/) (2022)
- [A challenge-based survey of e-recruitment recommendation systems](https://mlsanity.com/paper/2209.05112/) (2022)

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

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Model-as-a-Service (MaaS): A Survey](https://mlsanity.com/paper/2311.05804/) (2023)
- [Extreme Multi-Label Skill Extraction Training using Large Language Models](https://mlsanity.com/paper/2307.10778/) (2023)
- [CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations](https://mlsanity.com/paper/2503.02056/) (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)
- [Noise-Aware Named Entity Recognition for Historical VET Documents](https://mlsanity.com/paper/2601.00488/) (2026)
- [JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes](https://mlsanity.com/paper/2607.11715/) (2026)
- [Using generative AI to support standardization work -- the case of 3GPP](https://mlsanity.com/paper/2408.12611/) (2024)
- [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)
- [Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models](https://mlsanity.com/paper/2604.00006/) (2026)

## Abstract (arXiv)

We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as backbone. Most previous methods for similar tasks either need supervision or rely on heavy data-preprocessing and feature engineering. Directly prompting the latest conversational LLM for standard skills, however, is slow, costly and inaccurate. In contrast, SkillGPT utilizes a LLM to perform its tasks in steps via summarization and vector similarity search, to balance speed with precision. The backbone LLM of SkillGPT is based on Llama, free for academic use and thus useful for exploratory research and prototype development. Hence, our cost-free SkillGPT gives users the convenience of conversational SES, efficiently and reliably.