Data Augmentation Using Large Language Models: Methods, Challenges, and Perspectives

doi:10.1007/978-3-032-13869-9_5

2026-01-01

PRE

Agata Kozina *

Michał Pikus

Jarosław Wąs

DOI：10.1007/978-3-032-13869-9_5

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数据增强在提升机器学习性能中扮演着关键角色，尤其是在标签数据有限的情况下。随着大型语言模型（LLMs）的快速发展，其生成高质量合成数据的能力已引起广泛关注。本研究考察了利用LLMs进行数据增强的应用，特别是波兰大型语言模型Bielik。我们评估了其在生成多样化且上下文相关的合成文本以丰富金融领域数据集方面的能力，所用数据源自一家租赁公司。我们的研究涵盖了多种增强策略，包括通过改写（带有可调提示参数）进行的可控文本生成、对连续变量添加噪声（带有可修改的缩放）以及通过预设概率翻转进行的二元变量修改。我们评估了这些技术对整体模型性能的影响，同时应对了数据质量、偏见缓解以及将LLMs生成的数据整合到机器学习工作流中的伦理考量等挑战。最终，本研究为LLMs驱动的数据增强的演变应用和潜在改进提供了前瞻性视角，为实践者和研究者提供了有价值的见解。

Keyword:

Data augmentation

Large Language Models

Synthetic data

Financial sector

Machine learning

期刊

EMERGING CHALLENGES IN INTELLIGENT MANAGEMENT INFORMATION SYSTEMS, ECAI 2025-IMIS WORKSHOP, VOL 1

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agh university of krakow

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wroclaw university of economics & business

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543

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被引数: 1

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