What is the impact of diffusion-based tabular data augmentation on zero-shot performance of LLMs on the SuperG

Abstract

Abstract: The exponential growth of Large Language Models (LLMs) continues to highlight the need for efficient strategies to meet ever-expanding computational and data demands. This survey provides a comprehensive analysis of two complementary paradigms: Knowledge Distillation (KD) and Dataset Distillation (DD), both aimed at compressing LLMs while preserving their advanced reasoning capabilities and linguistic diversity. We first examine key methodologies in KD, such as task-specific alignment, rationale-based training, and multi-teacher frameworks, alongside DD techniques that synthesize compact, high

Research Question

What is the impact of diffusion-based tabular data augmentation on zero-shot performance of LLMs on the SuperGLUE benchmark when compared to CTGAN-generated data?

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