LLM driven Text-to-Table Generation through Sub-Tasks Guidance and Iterative Refinement

• URL: http://arxiv.org/abs/2508.08653v1
• Date: Tue, 12 Aug 2025 05:37:12 GMT
• Title: LLM driven Text-to-Table Generation through Sub-Tasks Guidance and Iterative Refinement
• Authors: Rajmohan C, Sarthak Harne, Arvind Agarwal,
• Abstract summary: This paper proposes an efficient system for Large Language Models (LLMs)-driven text-to-table generation that leverages novel prompting techniques.<n>We show that this custom task decomposition allows the model to address the problem in a stepwise manner and improves the quality of the generated table.<n>Our methods achieve strong results compared to baselines on two complex text-to-table generation datasets available in the public domain.
• Score: 1.373677542041849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transforming unstructured text into structured data is a complex task, requiring semantic understanding, reasoning, and structural comprehension. While Large Language Models (LLMs) offer potential, they often struggle with handling ambiguous or domain-specific data, maintaining table structure, managing long inputs, and addressing numerical reasoning. This paper proposes an efficient system for LLM-driven text-to-table generation that leverages novel prompting techniques. Specifically, the system incorporates two key strategies: breaking down the text-to-table task into manageable, guided sub-tasks and refining the generated tables through iterative self-feedback. We show that this custom task decomposition allows the model to address the problem in a stepwise manner and improves the quality of the generated table. Furthermore, we discuss the benefits and potential risks associated with iterative self-feedback on the generated tables while highlighting the trade-offs between enhanced performance and computational cost. Our methods achieve strong results compared to baselines on two complex text-to-table generation datasets available in the public domain.

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