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Prompt's Evolution for Language Model-Driven Data Generation

delete2025-12-08
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OA
AI
N
Nicolás Hidalgo *
P
Pablo Sáez
N
Nicolás Meneses
V
Víctor Reyes
E
Erika Rosas
DOI：10.3390/app152412911delete
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摘要

摘要

En 中文
在数据时代，数据生成量日益增长，其体量、种类和速度的动态性给处理系统带来了新的挑战。为实时处理数据，流处理系统（SPS）是实时数据分析系统的关键。然而，SPS必须在严格的服务质量（QoS）约束下运行，这要求其内部逻辑动态调整以维持性能。本研究通过采用进化方法指导提示学习，解决了使用大型语言模型（LLMs）进行自动文本数据生成的问题。该方法通过黑盒方式从参考数据集中生成合成数据，以自动发现提示。此方法旨在丰富训练数据集，并增强人工智能驱动的自适应SPS模型的泛化能力。
Keyword:
data generation
LLMs
metaheuristics
genetic algorithm

期刊

A
Applied Sciences-Basel
IF:
2.5
论文数:
7.6K
被引数:
4

机构

U
universitat politecnica de valencia
学者数:
1.1K
论文数: 512
被引数: 0
U
university diego portales
学者数:
1.5K
论文数: 1.5K
被引数: 23
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