Rethinking Skill Extraction in the Job Market Domain using Large Language Models

Introduction

This repo contains the code for the paper Rethinking Skill Extraction in the Job Market Domain using Large Language Models, published at the NLP4HR Workshop @ EACL2024. Don't hesitate to contact us if you have questions!

Usage

End-to-end experiments can be run with the following command

sh run.sh

Datasets

Datasets used for experiments can be found here. Additionally, you can download the processed annotation model by running the following command

python main.py --knn --dataset_name $DATASET_NAME

Running and Evaluation

Create an api_key.py and put your OpenAI API key under the variable API_KEY. Afterwards, you can run the experiments and evaluate the results using the following commands

python main.py --run --shots $NUM_SHOTS --knn --prompt_type $PROMPT_TYPE [--start_from_saved] [--exclude_empty] [--positive_only] --dataset_name $DATASET_NAME --model $MODEL

python main.py --eval --shots $NUM_SHOTS --knn --prompt_type $PROMPT_TYPE --dataset_name $DATASET_NAME --model $MODEL

Citation

@inproceedings{nguyen-etal-2024-rethinking,
    title = "Rethinking Skill Extraction in the Job Market Domain using Large Language Models",
    author = "Nguyen, Khanh  and
      Zhang, Mike  and
      Montariol, Syrielle  and
      Bosselut, Antoine",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Human Resources (NLP4HR 2024)",
    month = mar,
    year = "2024",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.nlp4hr-1.3",
}