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

• URL: http://arxiv.org/abs/2505.01636v1
• Date: Sat, 03 May 2025 00:05:01 GMT
• Title: Structured Prompting and Feedback-Guided Reasoning with LLMs for Data Interpretation
• Authors: Amit Rath,
• Abstract summary: Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and task generalization.<n>This paper introduces the STROT Framework, a method for structured prompting and feedback-driven transformation logic generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• TRUE: A Trustworthy Unified Explanation Framework for Large Language Model Reasoning [0.2538209532048867]
  Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their decision-making processes remain difficult to interpret.<n>We propose the Trustworthy Unified Explanation Framework (TRUE), which integrates executable reasoning verification, feasible-region directed acyclic graph (DAG) modeling, and causal failure mode analysis.
  arXiv  Detail & Related papers  (2026-02-21T17:00:54Z)
• Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement [66.51979814832332]
  model formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement.<n>Experiments demonstrate that model achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
  arXiv  Detail & Related papers  (2026-01-27T04:00:48Z)
• CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning [67.18702329644526]
  CoT Referring enhances model reasoning across modalities through a structured, chain-of-thought training data structure.<n>We restructure the training data to enforce a new output form, providing new annotations for existing datasets.<n>We also integrate detection and segmentation capabilities into a unified MLLM framework, training it with a novel adaptive weighted loss to optimize performance.
  arXiv  Detail & Related papers  (2025-10-03T08:50:21Z)
• LLM/Agent-as-Data-Analyst: A Survey [54.08761322298559]
  Large language models (LLMs) and agent techniques have brought a fundamental shift in the functionality and development paradigm of data analysis tasks.<n>LLMs enable complex data understanding, natural language, semantic analysis functions, and autonomous pipeline orchestration.
  arXiv  Detail & Related papers  (2025-09-28T17:31:38Z)
• Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates [56.73907811047611]
  Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities.<n>LLMs often fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent.<n>We introduce a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function callings.
  arXiv  Detail & Related papers  (2025-09-22T17:55:14Z)
• LLM-Symbolic Integration for Robust Temporal Tabular Reasoning [69.27153114778748]
  We introduce TempTabQA-C, a synthetic dataset designed for systematic and controlled evaluations.<n>This structured approach allows Large Language Models (LLMs) to generate and executesql queries, enhancing generalization and mitigating biases.
  arXiv  Detail & Related papers  (2025-06-06T05:14:04Z)
• DSR-Bench: Evaluating the Structural Reasoning Abilities of LLMs via Data Structures [20.596558700597644]
  Large language models (LLMs) are increasingly deployed for real-world tasks that fundamentally involve data manipulation.<n>A core requirement is the ability to perform structural reasoning--that is, to understand and reason about data relationships.<n>We introduce DSR-Bench, a novel benchmark evaluating LLMs' structural reasoning capabilities through data structures.
  arXiv  Detail & Related papers  (2025-05-29T23:24:53Z)
• SALAD: Improving Robustness and Generalization through Contrastive Learning with Structure-Aware and LLM-Driven Augmented Data [15.366930934639838]
  We propose SALAD, a novel approach to enhance model robustness and generalization.<n>Our method generates structure-aware and counterfactually augmented data for contrastive learning.<n>We validate our approach through experiments on three tasks: Sentiment Classification, Sexism Detection, and Natural Language Inference.
  arXiv  Detail & Related papers  (2025-04-16T15:40:10Z)
• Generating Structured Plan Representation of Procedures with LLMs [5.623006055588189]
  We introduce SOP Structuring ( SOPStruct), a novel approach to transform SOPs into structured representations.<n> SOPStruct produces a standardized representation of SOPs across different domains, reduces cognitive load, and improves user comprehension.<n>Our research highlights the transformative potential of Large Language Models to streamline process modeling.
  arXiv  Detail & Related papers  (2025-03-28T22:38:24Z)
• Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled Software [0.0]
  Large language model (LLM)-enabled systems are a significant challenge in software engineering.<n>We propose a probabilistic framework for systematically analyzing and improving these systems.
  arXiv  Detail & Related papers  (2025-01-10T22:42:06Z)
• StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs [78.84060166851805]
  StructTest is a novel benchmark that evaluates large language models (LLMs) on their ability to follow compositional instructions and generate structured outputs.<n> Assessments are conducted deterministically using a rule-based evaluator, which can be easily extended to new tasks and datasets.<n>We demonstrate that StructTest remains challenging even for top-performing models like Deepseek-V3/R1 and GPT-4o.
  arXiv  Detail & Related papers  (2024-12-23T22:08:40Z)
• Interpreting token compositionality in LLMs: A robustness analysis [10.777646083061395]
  Constituent-Aware Pooling (CAP) is a methodology designed to analyse how large language models process linguistic structures.<n>CAP intervenes in model activations through constituent-based pooling at various model levels.<n>Our findings highlight fundamental limitations in current transformer architectures regarding compositional semantics processing and model interpretability.
  arXiv  Detail & Related papers  (2024-10-16T18:10:50Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT)<n>We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data [117.13986738340027]
  We develop an emphIterative Reading-then-Reasoning(IRR) approach for solving question answering tasks based on structured data. Our approach can significantly boost the performance of ChatGPT and achieve comparable performance against the full-data supervised-tuning baselines.
  arXiv  Detail & Related papers  (2023-05-16T17:45:23Z)
• Autoregressive Structured Prediction with Language Models [73.11519625765301]
  We describe an approach to model structures as sequences of actions in an autoregressive manner with PLMs. Our approach achieves the new state-of-the-art on all the structured prediction tasks we looked at.
  arXiv  Detail & Related papers  (2022-10-26T13:27:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.