Can Large Language Model Predict Employee Attrition?

TL;DR

This paper demonstrates that fine-tuned GPT-3.5 significantly outperforms traditional machine learning models in predicting employee attrition, offering deeper insights and higher accuracy for HR management.

Contribution

It introduces the use of a fine-tuned GPT-3.5 model for employee attrition prediction and compares its performance to traditional ML classifiers.

Findings

GPT-3.5 achieved an F1-score of 0.92

Traditional models like SVM reached an F1-score of 0.82

LLMs can reveal deeper behavioral patterns in HR data

Abstract

Employee attrition poses significant costs for organizations, with traditional statistical prediction methods often struggling to capture modern workforce complexities. Machine learning (ML) advancements offer more scalable and accurate solutions, but large language models (LLMs) introduce new potential in human resource management by interpreting nuanced employee communication and detecting subtle turnover cues. This study leverages the IBM HR Analytics Attrition dataset to compare the predictive accuracy and interpretability of a fine-tuned GPT-3.5 model against traditional ML classifiers, including Logistic Regression, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost, and XGBoost. While traditional models are easier to use and interpret, LLMs can reveal deeper patterns in employee behavior. Our findings show that the fine-tuned GPT-3.5 model outperforms traditional methods with a precision of 0.91, recall of 0.94, and an F1-score of 0.92, while the best traditional model, SVM, achieved an F1-score of 0.82, with Random Forest and XGBoost reaching 0.80. These results highlight GPT-3.5's ability to capture complex patterns in attrition risk, offering organizations improved insights for retention strategies and underscoring the value of LLMs in HR applications.