mlsanityData to 7 Oct 2026

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

arXiv 2304.11060 ↗First posted 17 Apr 2023cs.CL cs.AIcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 17 Apr 2023.

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11 of 16 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  5. 2023Instruction Tuning with GPT-4cited by 676
  6. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
  7. 2018Learning Representations for Soft Skill Matchingcited by 19
  8. 2021DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matchingcited by 13
  9. 2022Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learningcited by 13
  10. 2022Skill Extraction from Job Postings using Weak Supervisioncited by 13
  11. 2022A challenge-based survey of e-recruitment recommendation systemscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 1; 2026: 4.

11 papers.