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

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arxiv 2304.11060 v2 pith:THSJVGPP submitted 2023-04-17 cs.CL cs.AI

Nan Li , Bo Kang , Tijl De Bie This is my paper · ORCID
classification cs.CLcs.AI
keywords skillgptbackboneconversationalextractionlanguagelargemodelskill
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling

    cs.CL 2026-07 conditional novelty 6.0 of 10

    STEP, with ROUTE embeddings and JobHop v2, sets new next-job prediction SOTA on four ESCO career-trajectory benchmarks by modeling inter-job time and education.

  2. JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes

    cs.CL 2026-07 conditional novelty 5.5 of 10

    JobHop v2 releases 355,315 ESCO-annotated career trajectories with temporal and education fields, extracted by a reasoning-controlled LLM pipeline from real VDAB resumes at near inter-annotator quality.

  3. (Towards) Scalable Reliable Automated Evaluation with Large Language Models

    cs.CL 2026-07 conditional novelty 4.0 of 10

    Multi-LLM pairwise Elo ranking with adjustable consensus thresholds produces rankings of competency profiles that average Spearman ρ≈0.83 with expert judgments.

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