Structured Prompting and Feedback-Guided Reasoning with LLMs for Data Interpretation (2505.01636v1)

Abstract: LLMs have demonstrated remarkable capabilities in natural language understanding and task generalization. However, their application to structured data analysis remains fragile due to inconsistencies in schema interpretation, misalignment between user intent and model output, and limited mechanisms for self-correction when failures occur. This paper introduces the STROT Framework (Structured Task Reasoning and Output Transformation), a method for structured prompting and feedback-driven transformation logic generation aimed at improving the reliability and semantic alignment of LLM-based analytical workflows. STROT begins with lightweight schema introspection and sample-based field classification, enabling dynamic context construction that captures both the structure and statistical profile of the input data. This contextual information is embedded in structured prompts that guide the model toward generating task-specific, interpretable outputs. To address common failure modes in complex queries, STROT incorporates a refinement mechanism in which the model iteratively revises its outputs based on execution feedback and validation signals. Unlike conventional approaches that rely on static prompts or single-shot inference, STROT treats the LLM as a reasoning agent embedded within a controlled analysis loop -- capable of adjusting its output trajectory through planning and correction. The result is a robust and reproducible framework for reasoning over structured data with LLMs, applicable to diverse data exploration and analysis tasks where interpretability, stability, and correctness are essential.

The STROT Framework: Enhancing LLM Reliability in Structured Data Analysis

In the paper titled The STROT Framework: Structured Prompting and Feedback-Guided Reasoning with LLMs for Data Interpretation, the author introduces a novel approach to address the inadequacies of LLMs when applied to structured data analysis. Despite significant advancements in LLMs concerning natural language tasks, challenges persist in their ability to accurately and reliably interpret structured data, such as tabular datasets or relational outputs. The STROT Framework proposes a structured interaction model between users and LLMs, highlighting schema grounding and iterative feedback mechanisms to improve semantic coherence.

Core Contributions

The essence of the STROT Framework lies in its three-phase interaction model:

1. Schema-Guided Context Construction: This initial step involves introspecting the dataset to create schema representations, including inferred data types and statistical summaries. By constructing a schema context containing type annotations and sample values, the framework aims to reduce semantic ambiguity and improve the alignment between the model's interpretations and the actual dataset.
2. Goal-Aligned Prompt Scaffolding: The paper underscores dynamic prompt generation based on the task goal and schema context. This approach aligns the model’s reasoning process under the constraints of structured and statistical characteristics of the input, ensuring that field selections and transformation logic closely match user intent.
3. Feedback-Based Output Refinement: Unlike static models, STROT treats the LLM as an adaptive reasoning component capable of self-correction through feedback loops. Upon encountering execution failures, the framework iteratively revises the model's outputs using feedback signals, thus enhancing robustness and reliability in complex analytical workflows.

Experimental Insights

The framework was empirically validated using a publicly available COVID-19 dataset, assessing its effectiveness in generating insights across diverse query scenarios, such as comparative analysis by WHO Region and country-level rankings. The outcomes demonstrated high execution validity, with the structured system achieving a substantially higher valid execution rate compared to a traditional one-shot prompting baseline. Moreover, the STROT framework exhibited resilience through its feedback-driven recovery process, correcting initial model errors without manual intervention.

Practical and Theoretical Implications

Practically, the STROT Framework has notable implications in sectors requiring high reliability and completeness in structured data analytics, such as scientific research, financial analysis, and enterprise data management. Its modular design allows the framework to handle schema variability and task complexity, providing a viable alternative to rigid program synthesis and potentially reducing dependency on domain-specific LLM fine-tuning.

Theoretically, the paper challenges the conventional viewpoint that LLMs function optimally in single-pass completion scenarios. By treating LLMs as agents capable of iterative reasoning, the research paves the way for future studies exploring advanced feedback interactions and multi-agent coordination in data-heavy contexts.

Future Directions

Looking forward, extending the STROT framework to incorporate semi-structured data and time series analysis presents a promising avenue for research. Additionally, integrating external validation heuristics could enhance output accuracy beyond mere syntactic corrections. By exploring these paths, researchers may leverage STROT to further refine the convergence between human-like reasoning and machine-driven data interpretation, ultimately bridging gaps in structured data analytics where precision and transparency are paramount.

In summation, the STROT Framework contributes significantly to the discourse on structured data reasoning with LLMs, offering a methodical approach that combines schema awareness with iterative feedback to refine model outputs, thereby overcoming traditional limitations in structured data tasks.