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arXiv · 2608.13866

Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classification

Benjamín Schindler·Gonzalo A. Ruz
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Abstract

Large language models (LLMs) can generate synthetic training data for text classification, but the quality of generated samples is heterogeneous: some fall in correct class regions of the embedding space while others land in peripheral or cross-class zones. We propose a geometric filtering framework that evaluates each LLM-generated sample by its Euclidean distance to real class examples in a sentence embedding space, selecting only geometrically consistent candidates. A soft weighting mechanism transforms filter scores into sample weights for classifier training. Evaluated across 13 datasets, 5 classifiers, 10 augmentation methods, and over 6,700 configurations, our method achieves +2.61 percentage points (pp) over SMOTE ($p<0.0001$, Cohen's $d=0.95$, 88.9% win rate). The approach generalizes to named entity recognition (+9.26pp, 100% win rate) without filter modification, and is robust across 5 LLMs from 4 providers. A key finding is that the simplest distance-based filter consistently outperforms complex multi-criteria alternatives.

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Benjamín Schindler, Gonzalo A. Ruz. 2026-08-14. Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classification. https://arxiv.org/abs/2608.13866

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