Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis

• URL: http://arxiv.org/abs/2507.12126v1
• Date: Wed, 16 Jul 2025 10:49:30 GMT
• Title: Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
• Authors: Payal Bhattad, Sai Manoj Pudukotai Dinakarrao, Anju Gupta,
• Abstract summary: This work introduces a principled evaluation framework for large language model (LLM) based text augmentation.<n> Empirical evaluations show that GPT-3.5 Turbo achieved the best balance of semantic fidelity, diversity, and generation efficiency.
• Score: 0.43988112145759295
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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