让大模型生成更稳定，提升表格文本分析的可靠性
CAST: Achieving Stable LLM-based Text Analysis for Data Analytics
- 用算法提示+先思考后输出，约束模型推理路径
- 在多个数据集上稳定性提升最高达16.2%
- 适合需要高可靠输出的数据分析场景
表格数据的文本分析依赖两大核心操作：摘要生成用于提取整体主题，标签标注用于行级标记。使用大语言模型（LLM）进行这些任务的关键局限在于难以满足数据分析所需的高输出稳定性要求。为此，我们提出CAST（Consistency via Algorithmic Prompting and Stable Thinking），通过约束模型的隐式推理路径来增强输出稳定性。CAST结合两项技术：(i) 算法提示，为有效推理转换施加程序化框架；(ii) 先思考后说话，强制在最终生成前做出显式中间承诺。为衡量进展，我们引入针对列表摘要和标签任务的稳定性度量指标CAST-S与CAST-T，并验证其与人工判断的一致性。在多个公开基准和多种LLM骨干网络上的实验表明，CAST在所有基线中始终表现最优，稳定性得分最高提升16.2%，同时保持或提升输出质量。
原文摘要 · Abstract (English)
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 stability among all baselines, improving Stability Score by up to 16.2\%, while maintaining or improving output quality.
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