SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model
This work addresses the need for cost-effective and accurate skill extraction for users in job matching and HR domains, though it appears incremental as it builds on existing LLM methods with optimizations.
The authors tackled the problem of skill extraction and standardization from job descriptions and user profiles by developing SkillGPT, a tool that uses an open-source large language model to perform the task efficiently and reliably, balancing speed with precision.
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.