arXiv:2605.24417v1 Announce Type: new Abstract: Supervised classification for tabular data remains a core machine learning task, yet its reliance on large labeled datasets limits applicability in data-scarce domains. For such few-shot scenarios, specialized methods like TabPFN - a state-of-the-art Prior-Data Fitted Network - have set a high standard by leveraging large-scale synthetic pretraining, though they still require a context of labeled examples to function. In contrast, Large Language Models (LLMs) could offer a more flexible alternative via zero- and few-shot in-context learning direc
The proliferation of Large Language Models (LLMs) and the increasing demand for efficient machine learning in data-scarce environments drive the exploration of LLMs for new tasks.
This development suggests LLMs could significantly reduce the need for extensive labeled datasets in specific machine learning tasks, lowering barriers to entry and accelerating application development.
LLMs are being evaluated as foundational models for tabular data classification, potentially disrupting traditional machine learning approaches and expanding the utility of zero- and few-shot learning.
- · LLM developers
- · Data-scarce industries
- · Analytics software providers
- · Traditional tabular ML methods relying on large datasets
- · Specialized few-shot learning models without generalizability
LLMs prove effective in binary tabular classification with minimal examples, broadening their application scope.
Reduced data labelling costs and faster model deployment in sectors with limited historical data.
Increased competition in AI model development as general-purpose LLMs begin to outperform specialized solutions across various domains.
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Read at arXiv cs.LG