走向更现实的职业道路预测:评估和方法
Elena Senger1,2, Yuri Campbell2, Rob van der Goot3
1MaiNLP, Center for Information and Language Processing, LMU Munich, Munich, Germany.
Frontiers in big data
|September 10, 2025
概括
这项研究比较了职业道路预测模型,包括大型语言模型 (LLM),发现先进的模型和微调可以提高准确性. 洞察力指导现实世界的职业预测系统部署.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 职业路径预测 (CPP) 对职业咨询和劳动力规划至关重要.
- 挑战包括数据变化,自由文本简历和有限的数据集.
- 现有的CPP模型需要全面评估.
研究的目的:
- 进行各种CPP模型的比较评估.
- 为LLMs提出新的模型变体和标准化的方法.
- 调查数据类型,合成数据和微调对CPP性能的影响.
主要方法:
- 线性投影,MLP,LSTM和LLM的比较分析.
- 在不同的输入设置 (标题,描述,自由文本) 中进行评估.
- 研究合成数据和微调策略.
主要成果:
- 建立了CPP模型的新性能基准.
- 揭示了不同建模策略和输入类型之间的权衡.
- 证明了新的MLP扩展和标准化的LLM方法的有效性.
结论:
- 大型语言模型显示了职业路径预测的前景.
- 微调和合成数据可以增强模型概括.
- 结果为部署有效的CPP系统提供了实际见解.
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