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Toward more realistic career path prediction: evaluation and methods.

Elena Senger^1,2, Yuri Campbell², Rob van der Goot³

¹MaiNLP, Center for Information and Language Processing, LMU Munich, Munich, Germany.

Frontiers in Big Data

|September 10, 2025

Summary

This study compares career path prediction models, including large language models (LLMs), finding that advanced models and fine-tuning improve accuracy. Insights guide real-world career prediction system deployment.

Keywords:

LLM career path prediction labor market recommendation synthetic data

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Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

Career path prediction (CPP) is vital for career counseling and workforce planning.
Challenges include data variability, free-text resumes, and limited datasets.
Existing CPP models require comprehensive evaluation.

Purpose of the Study:

To conduct a comparative evaluation of various CPP models.
To propose novel model variants and standardized approaches for LLMs.
To investigate the impact of data types, synthetic data, and fine-tuning on CPP performance.

Main Methods:

Comparative analysis of linear projection, MLP, LSTM, and LLM.
Evaluation across different input settings (titles, descriptions, free-text).
Investigation of synthetic data and fine-tuning strategies.

Main Results:

Established new performance baselines for CPP models.
Revealed trade-offs between different modeling strategies and input types.
Demonstrated the effectiveness of novel MLP extensions and standardized LLM approaches.

Conclusions:

Large language models show promise for career path prediction.
Fine-tuning and synthetic data can enhance model generalization.
Findings offer practical insights for deploying effective CPP systems.