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Structured Prompting and Feedback-Guided Reasoning with LLMs for Data Interpretation

Amit Rath

arXiv:2505.01636v1cs.AIcs.CLcs.LG

TL;DR

LLMs remain fragile in structured-data analysis, where schema interpretation, intent alignment, and failure recovery are central challenges. STROT uses schema-aware context, scaffolded planning, and feedback-driven revision; it reports a 95% first-attempt valid execution rate and automated recovery of remaining cases.

  • Problem

    LLMs remain unreliable on structured data, with schema interpretation, semantic alignment, and iterative recovery remaining challenging for analytical workflows.

    Source: §1 · p. 2, Motivation · p. 3

  • Method

    STROT combines schema-aware context construction, scaffolded analysis planning, transformation logic generation, and execution-feedback-driven revision in a controlled loop.

    Source: §3 · p. 6, §3.5 · p. 10, §5 · p. 19

  • Results

    95% valid execution rate on first attempt; STROT recovered the remaining cases through automated retries without additional supervision or retraining.

    Source: §5 · p. 19

  • Takeaways & Limitations

    STROT’s results support treating LLMs as modular agents in controlled reasoning loops for reliable, reproducible, and transparent structured-data analysis.

    Source: §5 · p. 19

    Cost estimates reflect API pricing as of February 2025 and typical STROT query tokenization patterns; enterprise batching and streaming may further reduce per-query cost.

    Source: Inference Efficiency and Input Scope · p. 17

Abstract

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Large language models (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.

1 Introduction

LLMs can generalize across natural-language tasks but remain unreliable on structured-data analysis, where schema grounding, intent alignment, and recovery from failure matter. STROT proposes a scaffolded, iterative approach that builds context from the data and revises outputs using execution feedback.

1 Introduction

  • LLMs show broad natural-language task capabilities, but structured-data tasks expose unreliable schema interpretation and semantically incoherent outputs, especially with unfamiliar schemas or multi-step transformations.The difficulty is pronounced in open-ended exploration, which requires schema comprehension, iterative analysis, and alignment with user intent.Source: §1 · p. 2

Problem Statement

  • Structured-data analysis requires schema stability, interpretable intermediate steps, and runtime robustness, while one-shot prompting lacks structured planning and post-hoc correction.The cited approaches can hallucinate or misuse fields, vary with prompt phrasing, and fail to recover from execution errors or semantic mismatches.Source: §1 · p. 2, Problem Statement · pp. 2–3

Motivation

  • STROT’s motivation is to replace rigid single-shot interaction with schema-aware interpretation, structured planning, and feedback-driven refinement that adjusts to data structure and task outcomes.This interaction model is framed as a way to bridge LLM generality with the precision and adaptability required for structured analysis.Source: Motivation · p. 3

Proposed Approach: The STROT Framework

  • STROT combines schema profiling, goal-aligned prompts, and feedback-based revision to support more reliable LLM analysis of structured data.It classifies fields from schemas and samples, constructs prompts using the analytical goal and data profile, and triggers revision when execution fails, returns empty output, or mismatches semantics.Source: Proposed Approach: The STROT Framework · pp. 3–4

2 Related Work

Prior work explores schema-grounded prompting and iterative refinement, but the cited approaches leave gaps in dynamic schema handling and structured-data exploration. STROT combines schema typing, intermediate planning, and self-correction in a framework for broader exploratory and interpretive tasks.

LLMs for Structured Data Interpretation

  • Schema-prompting and instruction-tuning studies address structured-data tasks, but cited approaches often assume single-pass correctness and may not scale to large or dynamic schemas.The passage names schema-alignment work [Herzig et al., 2020, Zhong et al., 2022], schema-grounded prompting by Lu et al. [2023], and instruction tuning by Liang et al. [2023].Source: LLMs for Structured Data Interpretation · p. 4

Feedback-Driven and Iterative Refinement Frameworks

  • Prior iterative methods include SELF-REFINE’s self-critique loop and CoCoGen’s execution-based refinement, while structured-data exploration requires schema-aware semantic accuracy.The cited comparison says much existing work focuses on natural language or transformation logic rather than structured data exploration.Source: Feedback-Driven and Iterative… · pp. 4–5

Comparative Summary

  • STROT combines schema typing, scaffolded planning, and iterative self-correction, unlike SQL-focused single-shot systems and Self-Refine’s lack of schema-specific reasoning and explicit planning.The comparison describes STROT as applicable to a broader range of exploratory and interpretive data tasks.Source: Comparative Summary · p. 5, Table 1 · p. 5

3 Methodology

STROT replaces single-pass interpretation with a modular, feedback-driven pipeline for structured tabular data. Its explicitly structured stages use intermediate outputs to incrementally build and verify reasoning.

3 Methodology

  • STROT treats the LLM as a model-agnostic reasoning component, using scaffolded prompts and controlled execution loops rather than fixed templates, handcrafted rules, or fine-tuned parameters.The pipeline is designed to handle schema variability, ambiguous instructions, and partial failure.Source: §3 · p. 6

3.1 Architecture Overview

  • The architecture has three interdependent components: schema-aware context construction, prompt-based task planning, and feedback-driven transformation synthesis and refinement.Together, they make the LLM a structured reasoning agent rather than a one-shot predictor.Source: §3.1 · pp. 6–7

3.2 Schema-Aware Context Construction

  • Schema context combines column types, statistical properties, and representative cell values in a structured, type-annotated object for prompt construction.The samples provide concrete column-domain examples without requiring full-row serialization.Source: §3.1 · p. 6, §3.2 · pp. 7–8

3.3 Prompt Scaffolding and Task Planning

  • The system conditions on the schema context to create an inspectable task plan with ordered steps, relevant fields, an operation type, and a rationale.The plan can be interpreted, verified, or revised before transformation logic is synthesized.Source: §3.3 · p. 9

3.4 Program Synthesis and Execution

  • A second-stage prompt translates the plan into executable transformation logic conditioned on the schema and expected output, which is then runtime-validated.Separating synthesis from planning supports checking whether the plan was translated into operational logic.Source: §3.4 · pp. 9–10

3.5 Feedback-Driven Refinement Loop

  • When execution fails, STROT feeds diagnostic traces back to the LLM for complete re-synthesis, repeating until valid output or the retry budget is reached.If no valid result is produced within the budget, the process returns a structured failure report.Source: §3.5 · pp. 10–11

3.6 System Execution Loop

  • The execution loop integrates schema grounding, planning, transformation synthesis, and feedback into a repeatable cycle that ends with a valid result or graceful fallback.Its stated priorities include clarity, adaptability across queries and dataset structures, and interpretable planning and correction.Source: §3.6 · p. 11, Figure 1 · p. 12

Additionally! Controlled Decoding and Determinism

  • STROT uses temperature 0.0–0.2 for planning and scaffolding, and 0.2–0.3 for synthesis and refinement, while bounding retries for predictability.The slightly higher synthesis temperature is intended to support variability in function generation and error recovery.Source: Additionally! Controlled Decoding and… · p. 13

4 Experiments

Across 20 analytical queries, STROT achieved higher valid execution and interpretability scores than one-shot prompting, while its refinement loop recovered nearly all failed cases. The experiments also report successful structured-data analyses and describe the framework’s input and inference efficiency.

4.1 Dataset and Setup

  • The COVID-19 dataset covered country- and region-level statistics across 187 countries, had no missing values, and supported tests at both grouping levels.The sample queries tested exploration and interpretation across country-level and region-level groupings.Source: §4.1 · p. 13

4.2 Qualitative Analysis: WHO Region Trends

  • For WHO regions, STROT successfully compared aggregated deaths and new cases, with the Americas and Europe showing the highest absolute values.The query used field-specific aggregation to produce a bar analysis without manual intervention.Source: Figure 2 · p. 14, §4.2 · p. 14

4.3 Country-Level Ranking

  • The country-ranking query ranked countries by recovered cases and produced aligned summaries of new cases, new deaths, and new recovered counts.The listed top countries included the United States, India, Brazil, and Russia.Source: §4.3 · p. 15

4.4 Iterative Reasoning and Correction

  • After an initial WHO Region query failed because of incorrect column naming, STROT revised the transformation to use “New cases” and succeeded on the second attempt.The execution trace guided the correction without user intervention.Source: §4.4 · p. 15

Quantitative Results and Baseline Comparison

Inference Efficiency and Input Scope

  • STROT uses schema summaries and typically 5–10 representative rows per column rather than sending the full dataset, with at most three model calls per query.Over 85% of queries succeeded on the first attempt, so most executions required only two calls.Source: Inference Efficiency and Input Scope · p. 16

Summary

  • Across query types, STROT reports schema-grounded field alignment, auditable plans, one-retry error recovery, generalization without task-specific customization, and deterministic execution.The experiments include ranking, temporal trends, regional aggregation, correlation, and outlier detection.Source: Summary · pp. 18–19

5 Conclusion

STROT combines schema profiling, scaffolded prompts, and bounded feedback-driven repair in an iterative reasoning loop for structured-data analysis. It reports a 95% first-attempt valid execution rate, with remaining cases recovered through retries, and proposes extensions beyond the current scope.

5 Conclusion

  • 95% valid execution on the first attempt; automated retries recovered the remaining cases without additional supervision or retraining.The reported results also describe improvements in execution robustness, semantic alignment, and interpretability over flat prompt baselines.Source: §5 · p. 19
  • STROT combines lightweight schema profiling and sample-driven context enrichment, scaffolded field-grounded analysis plans, and bounded feedback-driven repair.Its iterative loop includes context construction, task planning, transformation logic generation, and prompt-level introspection and self-revision after execution failures.Source: §5 · p. 19
  • The findings support treating LLMs for structured data as modular agents in controlled reasoning loops rather than infallible oracles.The paper presents compositional prompting, schema grounding, and runtime feedback as working together toward reliable, reproducible, and transparent outcomes.Source: §5 · p. 19
  • Future work proposes extending STROT to semi-structured data and time series, adding external validation heuristics, exploring multi-agent coordination, and benchmarking enterprise deployments.The paper also envisions applications in simulation-based forecasting, scientific workflow generation, and low-transformation logic automation.Source: §5 · pp. 19–20
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