{"slug": "geometric-filtering-of-llm-generated-samples-for-few-shot-text-classification", "title": "Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classification", "summary": "A new geometric filtering framework for selecting LLM-generated training samples improves few-shot text classification by +2.61 percentage points over SMOTE across 13 datasets, 5 classifiers, 10 augmentation methods, and over 6,700 configurations, with a 88.9% win rate and Cohen's d=0.95 (p<0.0001). The method also generalizes to named entity recognition (+9.26pp, 100% win rate) without modification and is robust across 5 LLMs from 4 providers, according to the arXiv preprint 2608.13866v1.", "body_md": "arXiv:2608.13866v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/geometric-filtering-of-llm-generated-samples-for-few-shot-text-classification", "canonical_source": "https://www.machinebrief.com/news/geometric-filtering-of-llm-generated-samples-for-few-shot-te-hopg", "published_at": "2026-08-17 04:00:00+00:00", "updated_at": "2026-08-17 04:42:05.243869+00:00", "lang": "en", "topics": ["machine-learning", "large-language-models", "artificial-intelligence"], "entities": ["arXiv", "SMOTE"], "alternates": {"html": "https://wpnews.pro/news/geometric-filtering-of-llm-generated-samples-for-few-shot-text-classification", "markdown": "https://wpnews.pro/news/geometric-filtering-of-llm-generated-samples-for-few-shot-text-classification.md", "text": "https://wpnews.pro/news/geometric-filtering-of-llm-generated-samples-for-few-shot-text-classification.txt", "jsonld": "https://wpnews.pro/news/geometric-filtering-of-llm-generated-samples-for-few-shot-text-classification.jsonld"}}