  With the ascent of large language models (LLM), natural language processing
has witnessed enhancements, such as LLM-based data augmentation. Nonetheless,
prior research harbors two primary concerns: firstly, a lack of contemplation
regarding whether the natural language generated by LLM (LLMNL) truly aligns
with human natural language (HNL), a critical foundational question; secondly,
an oversight that augmented data is randomly generated by LLM, implying that
not all data may possess equal training value, that could impede the
performance of classifiers. To address these challenges, we introduce the
scaling laws to intrinsically calculate LLMNL and HNL. Through extensive
experiments, we reveal slight deviations (approximately 0.2 Mandelbrot
exponent) from Mandelbrot's law in LLMNL, underscore a complexity advantage in
HNL, and supplement an interpretive discussion on language style. This
establishes a solid foundation for LLM's expansion. Further, we introduce a
novel data augmentation method for few-shot text classification, termed ZGPTDA,
which leverages fuzzy computing mechanisms driven by the conformity to scaling
laws to make decisions about GPT-4 augmented data. Extensive experiments,
conducted in real-world scenarios, confirms the effectiveness (improving F1 of
Bert and RoBerta by 7-10%) and competitiveness (surpassing recent AugGPT and
GENCO methods by about 2% accuracy on DeBerta) of ZGPTDA. In addition, we
reveal some interesting insights, e.g., Hilberg's law and Taylor's law can
impart more benefits to text classification, etc.
