CAST: Achieving Stable LLM-based Text Analysis for Data Analytics
arXiv:2602.15861v1 Announce Type: 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 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
arXiv:2602.15861v1 Announce Type: 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 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.
Executive Summary
CAST: Achieving Stable LLM-based Text Analysis for Data Analytics presents a framework (CAST) that enhances output stability for large language models (LLMs) in text analysis tasks. By combining algorithmic prompting and thinking-before-speaking techniques, CAST constrains the model's latent reasoning path, improving stability scores by up to 16.2% while maintaining output quality. Experiments across publicly available benchmarks validate CAST's effectiveness across multiple LLM backbones. This study addresses a critical limitation of LLM-based text analysis in data analytics, offering a promising solution for practitioners.
Key Points
- ▸ CAST is a framework that enhances output stability for LLMs in text analysis tasks.
- ▸ CAST combines algorithmic prompting and thinking-before-speaking techniques to constrain the model's latent reasoning path.
- ▸ Experiments demonstrate CAST's effectiveness in improving stability scores by up to 16.2% while maintaining output quality.
Merits
Strength
CAST addresses a critical limitation of LLM-based text analysis in data analytics, offering a promising solution for practitioners.
Demerits
Limitation
CAST's effectiveness may be specific to the selected LLM backbones and benchmarks, limiting its generalizability to other domains and models.
Expert Commentary
The CAST framework offers a promising solution to the challenge of output stability in LLM-based text analysis. By constraining the model's latent reasoning path, CAST demonstrates significant improvements in stability scores while maintaining output quality. However, its effectiveness may be specific to the selected LLM backbones and benchmarks. As the field continues to evolve, it is essential to explore the generalizability of CAST to other domains and models. Moreover, the development of CAST underscores the need for more research on LLM-based methods in data analytics, emphasizing the importance of stability and output quality in data-driven decision-making.
Recommendations
- ✓ Future research should investigate the generalizability of CAST to other domains and models, expanding its applicability in LLM-based text analysis.
- ✓ Developers and practitioners should consider incorporating CAST's framework into their workflows to improve the stability and output quality of LLM-based text analysis in data analytics.
