CAST: Achieving Stable LLM-based Text Analysis for Data Analytics

📰 ArXiv cs.AI

Learn how CAST achieves stable LLM-based text analysis for data analytics by ensuring consistency via algorithmic prompting

advanced Published 23 Apr 2026
Action Steps
  1. Implement CAST to stabilize LLM-based text analysis
  2. Use algorithmic prompting to ensure consistency in summarization and tagging tasks
  3. Evaluate the stability of LLM outputs using metrics such as consistency and accuracy
  4. Fine-tune LLMs using CAST to improve performance on specific datasets
  5. Integrate CAST with existing data analytics pipelines to enhance reliability
Who Needs to Know This

Data analysts and scientists can benefit from CAST to improve the reliability of their text analysis results, while AI engineers can use CAST to develop more stable LLM-based models

Key Insight

💡 CAST ensures consistency in LLM-based text analysis via algorithmic prompting, enabling more reliable data analytics

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📊 Improve text analysis stability with CAST! 🤖

Full Article

Title: CAST: Achieving Stable LLM-based Text Analysis for Data Analytics

Abstract:
arXiv:2602.15861v2 Announce Type: replace-cross Abstract: 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 introduce \textbf{CAST} (\textbf{C}onsistency via \textbf{A}lgorithmic Promptin
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