Decomposition-Driven Multi-Table Retrieval and Reasoning for Numerical Question Answering
Proposes DMRAL framework combining relation graphs and sub-question guided reasoning, boosting large-scale multi-table numerical QA by 24% retrieval and 55% accuracy.
Key Findings
Methodology
This paper introduces a relation graph-based preprocessing pipeline to model complex table relationships. The core modules include: a Table-Aligned Question Decomposer that extracts and aligns information needs; a Coverage-Aware Retriever employing learned semantic scoring via ColBERTv2 and FAISS for efficient candidate retrieval; and a Sub-question Guided Reasoner that incrementally generates and refines executable programs (SQL/Python). The relation graph captures joinability and unionability, facilitating effective multi-hop retrieval. The system employs chain-of-thought prompting and residual question generation to ensure comprehensive coverage and accurate numerical reasoning, validated on two large datasets with 73,688 and 109,949 tables respectively.
Key Results
- On two large-scale datasets, DMRAL achieved an average of 24% improvement in relevant table retrieval and 55% increase in answer accuracy over state-of-the-art methods. Specifically, in complex numerical questions requiring multi-table joins and unions, the model reduced retrieval errors by over 50% and improved reasoning correctness significantly. Ablation studies confirmed the importance of the relation graph, coverage scoring, and progressive reasoning modules, demonstrating their individual contributions to overall performance.
- Experimental results highlight that the relation graph effectively models complex inter-table relationships, the question decomposition ensures high-quality sub-questions, and the coverage verification reduces missing information. The combined approach results in robust, scalable, and interpretable multi-table QA suitable for real-world large datasets.
Significance
This work advances the frontier of large-scale multi-table question answering by integrating relation graph modeling, intelligent retrieval, and stepwise reasoning. It addresses critical bottlenecks in handling complex relationships, improving retrieval completeness, and ensuring numerical answer correctness. The framework's scalability and interpretability make it highly relevant for enterprise knowledge bases, scientific data analysis, and AI-driven decision support, fostering more autonomous and reliable data-driven applications.
Technical Contribution
The paper introduces a novel relation graph construction method that captures complex table relationships without relying on explicit schema constraints. It combines a multi-layer question decomposition strategy with a learned coverage scoring mechanism, leveraging ColBERTv2 for semantic matching. The sub-question guided reasoning module employs chain-of-thought prompting and residual question generation, enabling incremental and accurate program synthesis. These innovations collectively enhance retrieval precision, reasoning robustness, and system transparency, setting new benchmarks for large-scale multi-table QA.
Novelty
This is the first comprehensive framework that systematically integrates relation graph modeling, multi-level question decomposition, coverage-aware retrieval, and stepwise program generation tailored for large-scale, unstructured table collections. Unlike prior works limited to database schemas or small datasets, DMRAL is designed explicitly for real-world, heterogeneous table corpora, demonstrating significant performance gains and interpretability.
Limitations
- Despite its scalability, the relation graph construction may face challenges in extremely heterogeneous or noisy data environments, affecting relationship accuracy.
- The current model's computational cost remains high for real-time applications, requiring further optimization.
- Handling dynamic or evolving table collections necessitates incremental graph updates and retraining, which are yet to be explored.
Future Work
Future research will focus on integrating multi-modal data sources, such as text and images, into the relation graph framework. Additionally, exploring reinforcement learning to optimize retrieval and reasoning policies could further improve efficiency and accuracy. Extending the model to handle evolving datasets dynamically and reducing computational overhead are also promising directions.
AI Executive Summary
Large-scale table collections, such as web tables and data lakes, present significant challenges for automated numerical question answering (QA). Traditional methods, often designed for structured databases, struggle with the complexity and scale of unstructured data, especially when capturing intricate relationships among tables. Existing approaches like Text-to-SQL or open-domain MTQA are limited by assumptions of schema completeness and manageable size, leading to poor performance in real-world scenarios.
This paper introduces DMRAL, a Decomposition-driven Multi-table Retrieval and Answering framework, specifically tailored for large-scale, unstructured table collections. The core idea is to model complex inter-table relationships through a relation graph, enabling the system to understand and leverage joinability and unionability among tables. The framework employs a table-aligned question decomposer that extracts and aligns information needs, ensuring questions are broken into coherent sub-questions targeting specific tables or table groups.
To retrieve relevant tables efficiently, DMRAL uses a coverage-aware retriever that combines semantic scoring with a verification mechanism. This approach maximizes the likelihood of retrieving all relevant tables, reducing missed information. The sub-question guided reasoner then incrementally generates executable programs, such as SQL or Python scripts, to perform precise numerical calculations. Chain-of-thought prompting and residual question generation further enhance the accuracy of reasoning steps.
Extensive experiments on two large datasets, containing over 73,000 and 109,000 tables respectively, demonstrate that DMRAL outperforms existing methods by significant margins—24% in table retrieval accuracy and 55% in answer correctness. These results highlight the framework’s robustness, scalability, and interpretability, making it suitable for real-world applications in enterprise data analysis, scientific research, and AI-powered decision-making.
Despite these advances, challenges remain in handling extremely noisy or evolving datasets, optimizing computational costs, and extending multi-modal capabilities. Future work aims to address these issues by integrating dynamic graph updates, reinforcement learning, and multi-source data fusion, pushing the boundaries of large-scale, intelligent multi-table question answering.
Deep Analysis
Background
随着互联网和数据市场的快速发展，海量表格数据成为信息的重要载体。早期研究主要集中在关系数据库和结构化查询语言（SQL）上，代表性工作包括Text-to-SQL和数据库特定的问答系统。然而，这些方法依赖于完整的元数据和明确的关系，难以扩展到大规模无结构表格环境。近年来，开放域多表问答（MTQA）逐渐成为研究热点，旨在从分散的表格中自动检索和推理，代表性工作有MMQA、JAR等，但在复杂关系支持、多表联合和大规模检索方面仍存在瓶颈。传统方法多忽略表间复杂关系（如union和join），导致检索效果和推理准确性不足。为解决这些问题，本文提出了关系图建模和逐步推理的创新方案，旨在实现高效、准确的数值问答，满足实际应用需求。
Core Problem
当前多表问答面临多重挑战：一是复杂关系难以建模，导致检索和推理不完整；二是大规模表格检索效率低，误差率高；三是数值推理缺乏鲁棒性，容易出错。这些问题限制了多表问答在实际场景中的应用，尤其是在需要支持复杂关系和大规模数据的情况下。解决方案需在关系建模、检索效率和推理准确性方面实现突破，才能满足企业和科研对智能数据分析的需求。
Innovation
本文的核心创新包括：1）关系图预处理，捕获复杂关系（join、union）；2）多层次问句分解，确保问题完整性和表特异性；3）学习驱动的覆盖评分和验证机制，提升检索全面性；4）子问引导的逐步推理策略，增强数值推理的准确性。这些创新点区别于传统单表或数据库特定方法，特别适合大规模无结构表格环境，显著提升问答性能。
Methodology
- �� 关系图构建：利用unionability和joinability关系，预处理表格，形成关系网络。• 表对齐问句分解：采用句法分析提取信息需求，结合M3编码匹配列内容，优化问题分解。• 覆盖感知检索：基于学习模型（ColBERTv2）进行候选排序，利用覆盖评分确保检索全面。• 关系验证：构建连接表组，检测覆盖缺口，利用残差问答生成补充子问题，完善检索集。• 逐步推理：生成和优化程序（SQL/Python），确保数值推理正确。• 实验验证：在两个大规模数据集上，比较与现有方法的性能，进行消融分析。
Experiments
采用两个真实大规模表格数据集（73,688和109,949表）进行评估，指标包括表检索准确率和答案正确率。基线包括传统检索、JAR、MMQA等。模型超参数通过交叉验证确定，重点验证关系图效果、问句分解质量和推理策略。结果显示，DMRAL在复杂数值问答中表现优越，显著优于对比模型，验证其有效性和鲁棒性。
Results
模型在两个数据集上的平均表检索提升24%，答案正确率提升55%，在多表联合和复杂关系推理中表现出色。关系图有效支持复杂关系，问句分解确保高质量子问题，推理机制显著改善数值计算准确性。消融实验确认各模块贡献，整体表现优异。
Applications
该方法适用于企业数据仓库、金融分析、科研数据整合等场景，支持自动化多表信息提取和数值推理。只需提供表格集合和自然语言问题，即可实现高效、准确的答案生成，极大提升数据分析效率。未来还可结合多模态信息，拓展到图像、文本等多源数据的联合问答。
Limitations & Outlook
模型对极端复杂关系和超大规模表格的适应性仍有限，关系图构建和推理策略需优化。对噪声和元数据缺失敏感，鲁棒性不足。推理过程计算成本较高，未来需提升效率和泛化能力。
Plain Language Accessible to non-experts
想象你在一个大型厨房里准备一顿丰盛的晚餐。每个菜肴都用不同的食材和步骤，很多食材还可以组合成不同的菜。你需要找到所有相关的食材和步骤，才能做出美味的菜肴。这个过程就像多表问答，要从很多不同的表格（食材、调料、步骤）中找到需要的信息，然后按照正确的顺序组合起来，最后做出答案。就像厨师根据食谱逐步准备食材，系统也要逐步检索、组合信息，确保每个步骤都正确，才能做出完美的菜肴。这种方法让复杂的任务变得像做饭一样有条不紊，既高效又可靠。
ELI14 Explained like you're 14
想象你在学校的图书馆里找答案。有很多书，每本书都讲不同的内容。有时候，你需要从几本书里找到信息，然后把它们拼凑在一起，才能回答一个复杂的问题。比如，你想知道某个科学家的成就，但信息散落在不同的书里。你首先要找到所有相关的书，然后逐个阅读，最后把重要的点整理出来，得出答案。这就像在玩拼图游戏，要找到正确的拼块，把它们拼在一起，才能看到完整的图片。这个过程需要耐心和细心，就像系统在处理复杂问题时，要一步步找到相关信息，确保每个拼块都放对了位置，最终得到正确答案。
Glossary
关系图 (Relationship Graph)
一种用节点和边表示表格之间复杂关系的结构，帮助理解表间的连接方式。
用于捕获表的joinability和unionability关系。
表对齐问句分解器 (Table-Aligned Question Decomposer)
将复杂自然语言问题拆解成多个与表结构对应的子问题的模块。
提升问句分解质量，确保每个子问题对应特定表或表组。
覆盖感知检索 (Coverage-Aware Retriever)
通过学习模型评估表格对问题的覆盖程度，优化检索效果。
确保检索到的表能完整覆盖问题信息需求。
子问引导推理 (Sub-question Guided Reasoner)
逐步生成和优化推理程序，确保数值计算的正确性。
提升多表数值问答的准确率。
ColBERTv2
一种高效的深度匹配模型，用于评估文本或表格内容的相关性。
用于表格候选排序和覆盖评分。
Open Questions Unanswered questions from this research
- 1 如何进一步提升关系图在超大规模表格中的构建效率？
- 2 模型在极端复杂关系和高噪声环境下的鲁棒性如何增强？
- 3 多模态数据融合对多表问答性能的影响尚未充分研究。
Applications
Immediate Applications
企业智能数据分析
帮助企业自动检索和推理海量表格数据，实现快速决策支持。
科研数据整合
支持科研人员从多个数据源中自动提取和整合信息，加速科研进展。
Long-term Vision
全自动知识图谱构建
结合多模态信息，实现大规模知识图谱的自动更新和维护，推动AI理解能力提升。
Abstract
In this paper, we study the problem of numerical multi-table question answering (MTQA) over large-scale table collections (e.g., online data repositories). This task is essential in many analytical applications. Existing MTQA solutions, such as text-to-SQL or open-domain MTQA methods, are designed for databases and struggle when applied to large-scale table collections. The key limitations include: (1) Limited support for complex table relationships; (2) Ineffective retrieval of relevant tables at scale; (3) Inaccurate answer generation. To overcome these limitations, we propose DMRAL, a Decomposition-driven Multi-table Retrieval and Answering framework for MTQA over large-scale table collections, which consists of: (1) constructing a table relationship graph to capture complex relationships among tables; (2) Table-Aligned Question Decomposer and Coverage-Aware Retriever, which jointly enable the effective identification of relevant tables from large-scale corpora by enhancing the question decomposition quality and maximizing the question coverage of retrieved tables; and (3) Sub-question Guided Reasoner, which produces correct answers by progressively generating and refining the reasoning program based on sub-questions. Experiments on two MTQA datasets demonstrate that DMRAL significantly outperforms existing state-of-the-art MTQA methods, with an average improvement of 24% in table retrieval and 55% in answer accuracy.