SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model (2304.11060v2)

Abstract: We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source 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.

• The paper introduces SkillGPT, a RESTful API that employs a two-step process combining LLM summarization and vector similarity search for skill extraction and standardization.
• The methodology overcomes common challenges by eliminating heavy supervised learning and extensive preprocessing, thus ensuring cost-effective and precise skill matching.
• The study demonstrates SkillGPT’s applicability in recruitment with multi-lingual support and potential for further enhancements in AI-driven career planning and research prototyping.

Overview of SkillGPT: A RESTful API Service for Skill Extraction and Standardization Using a LLM

The paper "SkillGPT: a RESTful API service for skill extraction and standardization using a LLM" introduces a novel tool named SkillGPT, which leverages the capabilities of a LLM to facilitate automatic skill extraction and standardization from unstructured job descriptions and user profiles. This work emphasizes the use of an open-source LLM, Llama-based Vicuna-13B, to develop a tool that overcomes limitations of prior approaches, making it suitable for exploratory research and prototype development in academic settings.

Technical Approach

SkillGPT undertakes skill extraction and standardization (SES) by addressing the common pitfalls associated with prior methodologies such as the requirement for supervised learning, extensive data preprocessing, and feature engineering. The authors recognize the limitations of directly prompting recent conversational LLMs due to their tendency to generate inaccurate responses, including hallucinations, which severely limits their applicability in tasks like SES.

To counteract these issues, SkillGPT employs a two-step process. First, it summarizes the content of job descriptions or resumes. Subsequently, it leverages vector similarity search to match the summarized skills against a precomputed set of standardized skill embeddings derived from the ESCO taxonomy. This method strikes a balance between speed and precision, making it feasible for online SES tasks.

Implementation

The implementation architecture comprises an API service that effectively coordinates between various submodules. The initial phase involves system initialization, where ESCO entries are vectorized and stored in a database. Skill extraction and standardization are achieved by summarizing a job description or resume and performing vector similarity search for top-k matches from the ESCO database. The interactions are facilitated via a RESTful API as well as a graphical user interface, making the system accessible to different types of users.

Key Claiments and Results

The authors assert that SkillGPT achieves high precision in retrieving relevant ESCO codes with minimal computational cost. It supports multi-lingual capabilities, processing documents in English, French, and Dutch. The reported efficacy of SkillGPT is constrained by some limitations, such as performance variance across different languages and occasional inconsistencies in extracted codes when processed in different languages.

Implications and Future Directions

Practically, SkillGPT provides a cost-effective and streamlined solution for SES tasks in e-recruitment, impacting job recommendation systems and career path planning. Theoretically, it presents a scalable approach to utilizing LLMs in domain-specific applications without intensive computational overheads. SkillGPT’s open-source nature invites further exploration and adaptation by researchers, providing a flexible framework for SES tool development.

Looking forward, improvements in LLM readjustment methods promise advancements in systems like SkillGPT, potentially enhancing their precision and reducing linguistic biases. The paper suggests future work to address subtle skill identification, fine-tuning, and expanding support to additional languages, which will further solidify its utility across varied sectors and supports emerging needs in AI-driven methodologies.

In conclusion, SkillGPT represents a practical application of LLM technology, showcasing the potential for LLM-based SES tools to operate efficiently within real-world constraints while maintaining cost-effectiveness for academic use.