用分步引导与迭代优化提升大模型生成表格的准确性。
LLM driven Text-to-Table Generation through Sub-Tasks Guidance and Iterative Refinement
- 将文本转表格拆解为可管理的子任务，逐层推进。
- 通过迭代自反馈使表格结构更完整、内容更准确。
- 适合需要高质量结构化数据的科研与商业场景。
将非结构化文本转化为结构化数据是一项复杂任务，需具备语义理解、推理和结构认知能力。尽管大语言模型（LLMs）具有潜力，但在处理模糊或领域特定数据、保持表格结构、应对长输入以及数值推理方面仍存在挑战。本文提出一种高效的基于大模型的文本转表格系统，采用新颖的提示技术。具体包括：将文本转表格任务分解为可引导的子任务，并通过迭代自反馈对生成表格进行优化。实验表明，这种任务分解策略使模型能够分步解决难题，显著提升生成表格质量。同时，我们分析了迭代自反馈在性能提升与计算开销之间的权衡，指出其潜在优势与风险。该方法在两个公开复杂文本转表格数据集上均优于基线模型。
原文摘要 · Abstract (English)
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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