AI RESEARCH
CAST: Achieving Stable LLM-based Text Analysis for Data Analytics
arXiv CS.CL
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ArXi:2602.15861v2 Announce Type: replace Text analysis of tabular data relies on two core operations: \emph{summarization} for corpus-level theme extraction and \emph{tagging} for row-level labeling. A critical limitation of employing large language models (LLMs) for these tasks is their inability to meet the high standards of output stability demanded by data analytics. To address this challenge, we