Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation

Prompting large language models (LLMs) for data augmentation has recently become a common practice in few-shot NLP tasks. In this paper, we propose Chain-of-Thought Attribute Manipulation (CoTAM), a novel approach that generates new data from existing examples by only tweaking in the user-provided,...

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Veröffentlicht in:	arXiv.org 2024-05
Hauptverfasser:	Peng, Letian, Zhang, Yuwei, Shang, Jingbo
Format:	Artikel
Sprache:	eng
Schlagworte:	Large language models Sentences Training
Online-Zugang:	Volltext
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