先移植再生成：文本数据增强的新范式
Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
AI总结：
本文提出了一种名为LMTransplant的文本数据增强新范式，通过将种子文本移植到LLM扩展的上下文中并重新生成变体，在保留原文核心属性的同时，利用LLM知识生成更多样化的内容级变体，性能优于现有方法且具备良好可扩展性。
摘要：
数据增强是深度学习中的一项关键技术。像Back-translation这样的传统方法通常侧重于词汇层面的改写，主要产生语义相同的变体。尽管大型语言模型（LLMs）通过其“知识涌现”能力增强了文本增强，但控制这些输出的风格和结构仍然具有挑战性，并且需要细致的提示工程。在本文中，我们提出了LMTransplant，一种利用LLMs的新型文本增强范式。LMTransplant的核心思想是先移植再生成：将种子文本合并到由LLM扩展的上下文中，并要求LLM基于扩展的上下文重新生成变体。该策略允许模型通过充分利用嵌入在LLMs中的知识来创建更多样化和更具创造性的内容级变体，同时保留原始文本的核心属性。我们在各种与文本相关的任务中评估了LMTransplant，证明了其相对于现有文本增强方法的卓越性能。此外，随着增强数据规模的增加，LMTransplant展现出卓越的可扩展性。
英文摘要：
Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variations with the same semantics. While large language models (LLMs) have enhanced text augmentation by their "knowledge emergence" capability, controlling the style and structure of these outputs remains challenging and requires meticulous prompt engineering. In this paper, we propose LMTransplant, a novel text augmentation paradigm leveraging LLMs. The core idea of LMTransplant is transplant-then-regenerate: incorporating seed text into a context expanded by LLM, and asking the LLM to regenerate a variant based on the expanded context. This strategy allows the model to create more diverse and creative content-level variants by fully leveraging the knowledge embedded in LLMs, while preserving the core attributes of the original text. We evaluate LMTransplant across various text-related tasks, demonstrating its superior performance over existing text augmentation methods. Moreover, LMTransplant demonstrates exceptional scalability as the size of augmented data grows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
