Overlap Ratio Variations in Synthetic Tabular Pretraining and OOD Calibration Errors on Imbalanced Benchmarks

Abstract

Abstract: Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, arc

Research Question

How does varying the overlap ratio in synthetic tabular pretraining data impact the out-of-distribution calibration error of foundation models on imbalanced benchmarks?

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Quality Dimensions

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Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

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