How does domain-specific fine-tuning affect the performance of tabular foundation models on structured reasoni

Abstract

Abstract: We study a pipeline that curates reasoning data from initial structured data for improving long-context reasoning in large language models (LLMs). Our approach, \$\$pi\$2\$, constructs high-quality reasoning data through rigorous QA curation: 1) extracting and expanding tables from Wikipedia, 2) from the collected tables and relevant context, generating realistic and multi-hop analytical reasoning questions whose answers are automatically determined and verified through dual-path code execution, and 3) back-translating step-by-step structured reasoning traces as solutions of QA pairs given realistic

Research Question

How does domain-specific fine-tuning affect the performance of tabular foundation models on structured reasoning benchmarks like TabMWP or TabFact, measured by accuracy and robustness under distribution shifts?

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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.

Provenance