用于非结构化调查数据建模与分析的迭代增强与摘要精炼（IASR）评估
Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
AI总结：
本文提出用于LLM文本增强的原则性评估框架，包含可扩展性分析和迭代增强与摘要精炼（IASR），在真实主题建模任务中使主题粒度增加400%并消除重叠，验证了其有效性。
摘要：
文本数据增强是缓解自然语言处理（NLP）中数据稀疏性的广泛使用策略，特别是在有限样本阻碍有效语义建模的低资源环境下。虽然增强能提高输入多样性和下游可解释性，但现有技术通常缺乏在大型或迭代生成期间确保语义保持的机制，导致冗余和不稳定。本工作引入了一个基于大语言模型（LLM）文本增强的原则性评估框架，包含两个组件：（1）可扩展性分析，测量随增强量增加的语义一致性；（2）迭代增强与摘要精炼（IASR），评估递归复述周期中的语义漂移。在最先进LLM上的实证评估表明，GPT-3.5 Turbo在语义保真度、多样性和生成效率之间实现了最佳平衡。应用于使用BERTopic和GPT增强的少样本标注的真实世界主题建模任务，所提方法使主题粒度增加400%，并完全消除了主题重叠。这些发现验证了所提框架在实用NLP流程中对LLM增强进行结构化评估的效用。
英文摘要：
Text data augmentation is a widely used strategy for mitigating data sparsity in natural language processing (NLP), particularly in low-resource settings where limited samples hinder effective semantic modeling. While augmentation can improve input diversity and downstream interpretability, existing techniques often lack mechanisms to ensure semantic preservation during large-scale or iterative generation, leading to redundancy and instability. This work introduces a principled evaluation framework for large language model (LLM) based text augmentation, comprising two components: (1) Scalability Analysis, which measures semantic consistency as augmentation volume increases, and (2) Iterative Augmentation with Summarization Refinement (IASR), which evaluates semantic drift across recursive paraphrasing cycles. Empirical evaluations across state-of-the-art LLMs show that GPT-3.5 Turbo achieved the best balance of semantic fidelity, diversity, and generation efficiency. Applied to a real-world topic modeling task using BERTopic with GPT-enhanced few-shot labeling, the proposed approach results in a 400% increase in topic granularity and complete elimination of topic overlaps. These findings validated the utility of the proposed frameworks for structured evaluation of LLM-based augmentation in practical NLP pipelines.
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。
