VietJobs: A Vietnamese Job Advertisement Dataset

2026-03-05

AI Summary

This study addresses the scarcity of large-scale, publicly available Vietnamese job posting corpora by introducing VietJobs, the first comprehensive dataset comprising 48,092 structured job listings spanning 34 provinces and cities in Vietnam, with annotated fields including job category, salary, and required skills. The authors employ web crawling and cleaning techniques to construct the dataset and evaluate instruction-tuned large language models—such as Qwen2.5-7B-Instruct and Llama-SEA-LION-v3-8B-IT—on job classification and salary prediction tasks. Experimental results demonstrate that instruction tuning significantly enhances model performance under both few-shot and fine-tuning settings, highlighting the promise of multilingual and Vietnamese-specific language models for labor market analysis. This dataset and the accompanying benchmark framework fill a critical gap in Vietnamese natural language processing and workforce-related research.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Voting

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

VietJobs is the first large-scale, publicly available corpus of Vietnamese job advertisements, comprising 48,092 postings and over 15 million words collected from all 34 provinces and municipalities across Vietnam. The dataset provides extensive linguistic and structured information, including job titles, categories, salaries, skills, and employment conditions, covering 16 occupational domains and multiple employment types (full-time, part-time, and internship). Designed to support research in natural language processing and labour market analytics, VietJobs captures substantial linguistic, regional, and socio-economic diversity. We benchmark several generative large language models (LLMs) on two core tasks: job category classification and salary estimation. Instruction-tuned models such as Qwen2.5-7B-Instruct and Llama-SEA-LION-v3-8B-IT demonstrate notable gains under few-shot and fine-tuned settings, while highlighting challenges in multilingual and Vietnamese-specific modelling for structured labour market prediction. VietJobs establishes a new benchmark for Vietnamese NLP and offers a valuable foundation for future research on recruitment language, socio-economic representation, and AI-driven labour market analysis. All code and resources are available at: https://github.com/VinNLP/VietJobs.

Problem

• Vietnamese job advertisements
• large-scale dataset
• natural language processing
• labour market analytics
• multilingual modelling

Innovation

• Vietnamese job advertisement dataset
• large language models
• labour market analytics
• few-shot learning
• NLP benchmark