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[2602.15861] CAST: Achieving Stable LLM-based Text Analysis for Data Analytics
Summary
The paper presents CAST, a framework designed to improve the stability of LLM-based text analysis in data analytics by enhancing output consistency through algorithmic prompting and structured reasoning.
Why It Matters
As large language models (LLMs) become integral to data analytics, ensuring output stability is crucial for reliable analysis. CAST addresses this challenge, potentially transforming how LLMs are applied in practical data contexts, thereby enhancing data-driven decision-making.
Key Takeaways
- CAST framework improves output stability in LLM-based text analysis.
- Introduces Algorithmic Prompting and Thinking-before-Speaking for better reasoning.
- Demonstrates a 16.2% improvement in Stability Score while maintaining output quality.
- Validates new stability metrics aligned with human judgment.
- Enhances the applicability of LLMs in data analytics.
Computer Science > Computation and Language arXiv:2602.15861 (cs) [Submitted on 26 Jan 2026] Title:CAST: Achieving Stable LLM-based Text Analysis for Data Analytics Authors:Jinxiang Xie, Zihao Li, Wei He, Rui Ding, Shi Han, Dongmei Zhang View a PDF of the paper titled CAST: Achieving Stable LLM-based Text Analysis for Data Analytics, by Jinxiang Xie and 5 other authors View PDF HTML (experimental) 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 Prompting and \textbf{S}table \textbf{T}hinking), a framework that enhances output stability by constraining the model's latent reasoning path. CAST combines (i) Algorithmic Prompting to impose a procedural scaffold over valid reasoning transitions and (ii) Thinking-before-Speaking to enforce explicit intermediate commitments before final generation. To measure progress, we introduce \textbf{CAST-S} and \textbf{CAST-T}, stability metrics for bulleted summarization and tagging, and validate their alignment with human judgments. Experiments across publicly available benchmarks on multiple LLM backbones show that CAST consistently achieves the best...
