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.

翻译：我们提出SkillGPT，一种以开源大语言模型（LLM）为骨干，从自由格式职位描述与用户画像中进行技能提取与标准化（SES）的工具。以往处理类似任务的大多数方法要么需要监督学习，要么依赖繁重的数据预处理与特征工程。然而，直接提示最新对话型LLM获取标准技能的方式速度慢、成本高且精度低。相比之下，SkillGPT通过摘要生成与向量相似度搜索，以分步方式利用LLM执行任务，从而平衡速度与精度。其骨干LLM基于Llama，对学术研究免费开放，因此适用于探索性研究与原型开发。由此，我们提出的免费SkillGPT能够为用户提供高效可靠的对话式SES服务。