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Machine Learning (ML) in low-data settings remains an underappreciated yetcrucial problem. This challenge is pronounced in low-to-middle income countrieswhere access to large datasets is often limited or even absent. Hence, dataaugmentation methods to increase the sample size of datasets needed for ML arekey to unlocking the transformative potential of ML in data-deprived regionsand domains. Unfortunately, the limited training set constrains traditionaltabular synthetic data generators in their ability to generate a large anddiverse augmented dataset needed for ML tasks. To address this technicalchallenge, we introduce CLLM, which leverages the prior knowledge of LargeLanguage Models (LLMs) for data augmentation in the low-data regime. Whilediverse, not all the data generated by LLMs will help increase utility for adownstream task, as for any generative model. Consequently, we introduce aprincipled curation process, leveraging learning dynamics, coupled withconfidence and uncertainty metrics, to obtain a high-quality dataset.Empirically, on multiple real-world datasets, we demonstrate the superiorperformance of LLMs in the low-data regime compared to conventional generators.We further show our curation mechanism improves the downstream performance forall generators, including LLMs. Additionally, we provide insights andunderstanding into the LLM generation and curation mechanism, shedding light onthe features that enable them to output high-quality augmented datasets. CLLMpaves the way for wider usage of ML in data scarce domains and regions, byallying the strengths of LLMs with a robust data-centric approach.

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes