We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles. SkillGPT utilizes an open-source Large Language Model (LLM) as its backbone, specifically based on Llama which is free for academic use. Unlike previous methods that require supervision or rely on heavy data-preprocessing and feature engineering, SkillGPT performs its tasks in steps through summarization and vector similarity search. This approach balances speed with precision providing users with the convenience of conversational SES efficiently and reliably. The design choices of SkillGPT were carefully considered and experimented with. The justification of these choices as well as an ablation study will be presented in an extended manuscript. The main components of SkillGPT include tools such as summarization and vector similarity search which can be flexible to accommodate other options. SkillGPT supports various use cases including different document types (job description/user resume), ESCO concept types (Skill/Occupation/Occupation group), and languages (En/Fr/Nl). The current version allows for 18 possible use cases. Users can input free-styled documents in French, English or Dutch and choose the document type between job description and user profile. By clicking "Summarize," the backbone LLM distills the skills contained in the document providing an output skill list in the same language as the original input. Additionally users can choose which ESCO concept type to standardize the free-style skill descriptions; the corresponding most plausible ESCO terminologies will be returned however it should be noted that extracted codes for the same content in different languages may differ somewhat. While SkillGPT is efficient economical and often delivers truthful and plausible results it does have some limitations; treating summarized text as a single document might cause subtle skills to be lost since dominant qualities may overshadow them; furthermore there are many options for optimizing performance of LLMs that have not been thoroughly examined due to time limitations and rapidly evolving nature of LLM utilization. In future authors plan to address these limitations by considering qualitative & quantitative evaluations on SES & various downstream tasks in e-recruitment recommendation; they also aim to optimize SkillGPT for smaller languages & support full range of 25 European Union languages.
- SkillGPT is a tool for skill extraction and standardization from job descriptions and user profiles
- It utilizes an open-source Large Language Model (LLM) called Llama
- SkillGPT performs its tasks through summarization and vector similarity search
- The design choices of SkillGPT were carefully considered and experimented with
- It supports various use cases including different document types, ESCO concept types, and languages
- Users can input free-styled documents in French, English or Dutch and choose the document type between job description and user profile
- The backbone LLM distills the skills contained in the document providing an output skill list in the same language as the original input
- Users can choose which ESCO concept type to standardize the free-style skill descriptions
- Extracted codes for the same content in different languages may differ somewhat
- SkillGPT has limitations such as potential loss of subtle skills when treating summarized text as a single document
- Future plans include addressing these limitations, qualitative & quantitative evaluations on SES & various downstream tasks, optimizing for smaller languages, and supporting all 25 European Union languages.
SkillGPT is a tool that helps with understanding and organizing skills from job descriptions and user profiles. It uses a special program called Llama to do this. SkillGPT works by summarizing the information and comparing it to find similar skills. The creators of SkillGPT thought carefully about how it should work and tried different ideas. It can be used for different types of documents, concepts, and languages. Users can input documents in French, English, or Dutch and choose between job descriptions or user profiles. The main program in SkillGPT finds the skills in the document and gives a list of them in the same language as the original document. Users can also choose how they want the skills to be described using ESCO concept types. Sometimes, when looking at different languages, the codes for the same skill might be slightly different. SkillGPT has some limitations, like not being able to understand subtle skills when summarizing text as one document. In the future, there are plans to improve these limitations and test SkillGPT on different tasks."
Definitions- Tool: Something that helps you do a task.
- Job descriptions: Information about what kind of work someone does.
- User profiles: Information about a person's interests or abilities.
- Summarization: Making something shorter but still keeping important information.
- Vector similarity search: Comparing things to see if they are similar based on their characteristics.
- Language model: A program that understands and uses words in a certain language.
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Introducing SkillGPT: An Open-Source Tool for Skill Extraction and Standardization
The world of recruitment is ever-evolving, with new technologies emerging to make the process easier and more efficient. One such technology is SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles. It utilizes an open-source Large Language Model (LLM) as its backbone, specifically based on Llama which is free for academic use. This article will provide an overview of how SkillGPT works, its design choices, current capabilities, limitations, and future plans.
How Does SkillGPT Work?
SkillGPT performs its tasks in steps through summarization and vector similarity search. This approach balances speed with precision providing users with the convenience of conversational SES efficiently and reliably. The main components of SkillGPT include tools such as summarization and vector similarity search which can be flexible to accommodate other options.
Users can input free-styled documents in French, English or Dutch and choose the document type between job description and user profile. By clicking "Summarize," the backbone LLM distills the skills contained in the document providing an output skill list in the same language as the original input. Additionally users can choose which ESCO concept type to standardize the free-style skill descriptions; the corresponding most plausible ESCO terminologies will be returned however it should be noted that extracted codes for the same content in different languages may differ somewhat.
Design Choices & Current Capabilities
The design choices of SkillGPT were carefully considered and experimented with before being released to ensure accuracy while also balancing speed & efficiency; this includes using LLMs instead of traditional methods like supervised learning or heavy data preprocessing & feature engineering which are often time consuming & costly processes respectively . Currently SkillGPT supports various use cases including different document types (job description/user resume), ESCO concept types (Skill/Occupation/Occupation group),and languages (En/Fr/Nl). The current version allows for 18 possible use cases across these three categories giving users plenty of flexibility when it comes to extracting & standardizing skills from their documents .
Limitations & Future Plans
While SkillGPT is efficient economical and often delivers truthful & plausible results it does have some limitations; treating summarized text as a single document might cause subtle skills to be lost since dominant qualities may overshadow them; furthermore there are many options for optimizing performance of LLMs that have not been thoroughly examined due to time limitations & rapidly evolving nature of LLM utilization . In future authors plan to address these limitations by considering qualitative & quantitative evaluations on SES& various downstream tasks in e-recruitment recommendation ; they also aim to optimizeSkill GTPfor smaller languages& support full range of 25 European Union languages .
Conclusion
Overall ,Skill GTPis a powerful tool that provides users with fast , reliable ,and accurate results when it comes extracting&standardizing skills from free - style job descriptions or user profiles . Its design choices were carefully consideredto balance speedwith precisionwhile also allowingfor flexibility across multipleusecases&languages . While thereare some limitationsto consider ,the authors planon addressingtheseinfuture versionsof this tool so stay tuned !
